Class: Rumale::LinearModel::LinearRegression

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

Overview

LinearRegression is a class that implements ordinary least square linear regression with mini-batch stochastic gradient descent optimization.

Examples:

estimator =
  Rumale::LinearModel::LinearRegression.new(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(fit_bias: false, bias_scale: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil, n_jobs: nil, random_seed: nil) ⇒ LinearRegression

Create a new ordinary least square linear regressor.

Parameters:

  • 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/linear_regression.rb', line 45

def initialize(fit_bias: false, bias_scale: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil,
               n_jobs: nil, random_seed: nil)
  check_params_float(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(max_iter: max_iter, batch_size: batch_size)
  keywd_args = method(:initialize).parameters.map { |_t, arg| [arg, binding.local_variable_get(arg)] }.to_h.merge(reg_param: 0.0)
  super(keywd_args)
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/linear_regression.rb', line 26

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/linear_regression.rb', line 30

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/linear_regression.rb', line 22

def weight_vec
  @weight_vec
end

Instance Method Details

#fit(x, y) ⇒ LinearRegression

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:



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# File 'lib/rumale/linear_model/linear_regression.rb', line 61

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 LinearRegression.



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

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/linear_regression.rb', line 105

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/linear_regression.rb', line 89

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