Class: Rumale::LinearModel::Lasso

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

Overview

Lasso is a class that implements Lasso 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. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” Proc. COMPSTAT’10, pp. 177–186, 2010.

Examples:

estimator =
  Rumale::LinearModel::Lasso.new(reg_param: 0.1, max_iter: 1000, batch_size: 20, 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, bias_scale: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil, n_jobs: nil, random_seed: nil) ⇒ Lasso

Create a new Lasso regressor.

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: 10) —

    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/lasso.rb', line 49

def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0, max_iter: 1000, batch_size: 10, 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)
  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, batch_size: batch_size)
  super
end

Instance Attribute Details

#bias_term ⇒ Numo::DFloat (readonly)

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

Returns:

  • (Numo::DFloat) —

    (shape: [n_outputs])



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# File 'lib/rumale/linear_model/lasso.rb', line 29

def bias_term
  @bias_term
end

#rng ⇒ Random (readonly)

Return the random generator for random sampling.

Returns:

  • (Random) 


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# File 'lib/rumale/linear_model/lasso.rb', line 33

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector.

Returns:

  • (Numo::DFloat) —

    (shape: [n_outputs, n_features])



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# File 'lib/rumale/linear_model/lasso.rb', line 25

def weight_vec
  @weight_vec
end

Instance Method Details

#fit(x, y) ⇒ Lasso

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, n_outputs]) The target values to be used for fitting the model.

Returns:

  • (Lasso) —

    The learned regressor itself.



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# File 'lib/rumale/linear_model/lasso.rb', line 64

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 Lasso.



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# File 'lib/rumale/linear_model/lasso.rb', line 98

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/lasso.rb', line 107

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/lasso.rb', line 91

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