Class: Rumale::PolynomialModel::FactorizationMachineRegressor

Inherits:
BaseFactorizationMachine show all
Includes:
Base::Regressor
Defined in:
lib/rumale/polynomial_model/factorization_machine_regressor.rb

Overview

FactorizationMachineRegressor is a class that implements Factorization Machine with stochastic gradient descent (SGD) optimization.

Reference

    1. Rendle, “Factorization Machines with libFM,” ACM TIST, vol. 3 (3), pp. 57:1–57:22, 2012.

    1. Rendle, “Factorization Machines,” Proc. ICDM’10, pp. 995–1000, 2010.

Examples:

estimator =
  Rumale::PolynomialModel::FactorizationMachineRegressor.new(
   n_factors: 10, reg_param_linear: 0.1, reg_param_factor: 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(n_factors: 2, reg_param_linear: 1.0, reg_param_factor: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil, n_jobs: nil, random_seed: nil) ⇒ FactorizationMachineRegressor

Create a new regressor with Factorization Machine.

Parameters:

  • n_factors (Integer) (defaults to: 2) —

    The maximum number of iterations.

  • reg_param_linear (Float) (defaults to: 1.0) —

    The regularization parameter for linear model.

  • reg_param_factor (Float) (defaults to: 1.0) —

    The regularization parameter for factor matrix.

  • 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/polynomial_model/factorization_machine_regressor.rb', line 55

def initialize(n_factors: 2, reg_param_linear: 1.0, reg_param_factor: 1.0,
               max_iter: 1000, batch_size: 10, optimizer: nil, n_jobs: nil, random_seed: nil)
  check_params_float(reg_param_linear: reg_param_linear, reg_param_factor: reg_param_factor)
  check_params_integer(n_factors: n_factors, max_iter: max_iter, batch_size: batch_size)
  check_params_type_or_nil(Integer, n_jobs: n_jobs, random_seed: random_seed)
  check_params_positive(n_factors: n_factors, reg_param_linear: reg_param_linear, reg_param_factor: reg_param_factor,
                        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(loss: nil)
  super(keywd_args)
end

Instance Attribute Details

#bias_term ⇒ Numo::DFloat (readonly)

Return the bias term for Factoriazation Machine.

Returns:

  • (Numo::DFloat) —

    (shape: [n_outputs])



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# File 'lib/rumale/polynomial_model/factorization_machine_regressor.rb', line 35

def bias_term
  @bias_term
end

#factor_mat ⇒ Numo::DFloat (readonly)

Return the factor matrix for Factorization Machine.

Returns:

  • (Numo::DFloat) —

    (shape: [n_outputs, n_factors, n_features])



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# File 'lib/rumale/polynomial_model/factorization_machine_regressor.rb', line 27

def factor_mat
  @factor_mat
end

#rng ⇒ Random (readonly)

Return the random generator for random sampling.

Returns:

  • (Random) 


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# File 'lib/rumale/polynomial_model/factorization_machine_regressor.rb', line 39

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector for Factorization Machine.

Returns:

  • (Numo::DFloat) —

    (shape: [n_outputs, n_features])



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# File 'lib/rumale/polynomial_model/factorization_machine_regressor.rb', line 31

def weight_vec
  @weight_vec
end

Instance Method Details

#fit(x, y) ⇒ FactorizationMachineRegressor

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/polynomial_model/factorization_machine_regressor.rb', line 71

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
    @factor_mat = Numo::DFloat.zeros(n_outputs, @params[:n_factors], n_features)
    @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| @factor_mat[n, true, true], @weight_vec[n, true], @bias_term[n] = models[n] }
    else
      n_outputs.times { |n| @factor_mat[n, true, true], @weight_vec[n, true], @bias_term[n] = partial_fit(x, y[true, n]) }
    end
  else
    @factor_mat, @weight_vec, @bias_term = partial_fit(x, y)
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about FactorizationMachineRegressor.



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# File 'lib/rumale/polynomial_model/factorization_machine_regressor.rb', line 113

def marshal_dump
  { params: @params,
    factor_mat: @factor_mat,
    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/polynomial_model/factorization_machine_regressor.rb', line 123

def marshal_load(obj)
  @params = obj[:params]
  @factor_mat = obj[:factor_mat]
  @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/polynomial_model/factorization_machine_regressor.rb', line 100

def predict(x)
  check_sample_array(x)
  linear_term = @bias_term + x.dot(@weight_vec.transpose)
  factor_term = if @weight_vec.shape[1].nil?
                  0.5 * (@factor_mat.dot(x.transpose)**2 - (@factor_mat**2).dot(x.transpose**2)).sum(0)
                else
                  0.5 * (@factor_mat.dot(x.transpose)**2 - (@factor_mat**2).dot(x.transpose**2)).sum(1).transpose
                end
  linear_term + factor_term
end