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.



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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).



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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.



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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.



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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.



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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.



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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.



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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.



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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