Class: Rumale::LinearModel::Ridge

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

Overview

Ridge is a class that implements Ridge Regression with mini-batch stochastic gradient descent optimization or singular value decomposition.

Examples:

estimator =
  Rumale::LinearModel::Ridge.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)

# If Numo::Linalg is installed, you can specify 'svd' for the solver option.
require 'numo/linalg/autoloader'
estimator = Rumale::LinearModel::Ridge.new(reg_param: 0.1, solver: 'svd')
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, solver: 'sgd', n_jobs: nil, random_seed: nil) ⇒ Ridge

Create a new Ridge 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. If solver = ‘svd’, this parameter is ignored.

  • batch_size (Integer) (defaults to: 10) —

    The size of the mini batches. If solver = ‘svd’, this parameter is ignored.

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

    The optimizer to calculate adaptive learning rate. If nil is given, Nadam is used. If solver = ‘svd’, this parameter is ignored.

  • solver (String) (defaults to: 'sgd') —

    The algorithm to calculate weights. (‘sgd’ or ‘svd’). ‘sgd’ uses the stochastic gradient descent optimization. ‘svd’ performs singular value decomposition of samples.

  • 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 or the solver is ‘svd’.

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

    The seed value using to initialize the random generator.



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# File 'lib/rumale/linear_model/ridge.rb', line 57

def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil,
               solver: 'sgd', 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_string(solver: solver)
  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)
  keywd_args = method(:initialize).parameters.map { |_t, arg| [arg, binding.local_variable_get(arg)] }.to_h
  keywd_args.delete(:solver)
  super(keywd_args)
  @params[:solver] = solver != 'svd' ? 'sgd' : 'svd'
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/ridge.rb', line 31

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/ridge.rb', line 35

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/ridge.rb', line 27

def weight_vec
  @weight_vec
end

Instance Method Details

#fit(x, y) ⇒ Ridge

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:

  • (Ridge) —

    The learned regressor itself.



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# File 'lib/rumale/linear_model/ridge.rb', line 76

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

  if @params[:solver] == 'svd' && enable_linalg?
    fit_svd(x, y)
  else
    fit_sgd(x, y)
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about Ridge.



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# File 'lib/rumale/linear_model/ridge.rb', line 101

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/ridge.rb', line 110

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/ridge.rb', line 94

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