Class: Rumale::PolynomialModel::FactorizationMachineClassifier

Inherits:
BaseFactorizationMachine show all
Includes:
Base::Classifier
Defined in:
lib/rumale/polynomial_model/factorization_machine_classifier.rb

Overview

FactorizationMachineClassifier is a class that implements Factorization Machine with stochastic gradient descent (SGD) optimization. For multiclass classification problem, it uses one-vs-the-rest strategy.

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::FactorizationMachineClassifier.new(
   n_factors: 10, loss: 'hinge', reg_param_linear: 0.001, reg_param_factor: 0.001,
   max_iter: 5000, batch_size: 50, random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Classifier

#score

Constructor Details

#initialize(n_factors: 2, loss: 'hinge', reg_param_linear: 1.0, reg_param_factor: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil, n_jobs: nil, random_seed: nil) ⇒ FactorizationMachineClassifier

Create a new classifier with Factorization Machine.

Parameters:

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

    The maximum number of iterations.

  • loss (String) (defaults to: 'hinge') —

    The loss function (‘hinge’ or ‘logistic’).

  • 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 and predict methods in parallel. If nil is given, the methods do 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_classifier.rb', line 62

def initialize(n_factors: 2, loss: 'hinge', 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_string(loss: loss)
  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)
  super
  @classes = nil
end

Instance Attribute Details

#bias_term ⇒ Numo::DFloat (readonly)

Return the bias term for Factoriazation Machine.

Returns:

  • (Numo::DFloat) —

    (shape: [n_classes])



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 37

def bias_term
  @bias_term
end

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_classes])



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 41

def classes
  @classes
end

#factor_mat ⇒ Numo::DFloat (readonly)

Return the factor matrix for Factorization Machine.

Returns:

  • (Numo::DFloat) —

    (shape: [n_classes, n_factors, n_features])



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 29

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_classifier.rb', line 45

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector for Factorization Machine.

Returns:

  • (Numo::DFloat) —

    (shape: [n_classes, n_features])



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 33

def weight_vec
  @weight_vec
end

Instance Method Details

#decision_function(x) ⇒ Numo::DFloat

Calculate confidence scores for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to compute the scores.

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples]) Confidence score per sample.



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 120

def decision_function(x)
  check_sample_array(x)
  linear_term = @bias_term + x.dot(@weight_vec.transpose)
  factor_term = if @classes.size <= 2
                  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

#fit(x, y) ⇒ FactorizationMachineClassifier

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]) The labels to be used for fitting the model.

Returns:



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 80

def fit(x, y)
  check_sample_array(x)
  check_label_array(y)
  check_sample_label_size(x, y)

  @classes = Numo::Int32[*y.to_a.uniq.sort]
  n_classes = @classes.size
  _n_samples, n_features = x.shape

  if n_classes > 2
    @factor_mat = Numo::DFloat.zeros(n_classes, @params[:n_factors], n_features)
    @weight_vec = Numo::DFloat.zeros(n_classes, n_features)
    @bias_term = Numo::DFloat.zeros(n_classes)
    if enable_parallel?
      # :nocov:
      models = parallel_map(n_classes) do |n|
        bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
        partial_fit(x, bin_y)
      end
      # :nocov:
      n_classes.times { |n| @factor_mat[n, true, true], @weight_vec[n, true], @bias_term[n] = models[n] }
    else
      n_classes.times do |n|
        bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
        @factor_mat[n, true, true], @weight_vec[n, true], @bias_term[n] = partial_fit(x, bin_y)
      end
    end
  else
    negative_label = y.to_a.uniq.min
    bin_y = Numo::Int32.cast(y.ne(negative_label)) * 2 - 1
    @factor_mat, @weight_vec, @bias_term = partial_fit(x, bin_y)
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about FactorizationMachineClassifier.



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 167

def marshal_dump
  { params: @params,
    factor_mat: @factor_mat,
    weight_vec: @weight_vec,
    bias_term: @bias_term,
    classes: @classes,
    rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 178

def marshal_load(obj)
  @params = obj[:params]
  @factor_mat = obj[:factor_mat]
  @weight_vec = obj[:weight_vec]
  @bias_term = obj[:bias_term]
  @classes = obj[:classes]
  @rng = obj[:rng]
  nil
end

#predict(x) ⇒ Numo::Int32

Predict class labels for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Predicted class label per sample.



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 135

def predict(x)
  check_sample_array(x)
  return Numo::Int32.cast(decision_function(x).ge(0.0)) * 2 - 1 if @classes.size <= 2

  n_samples = x.shape[0]
  decision_values = decision_function(x)
  predicted = if enable_parallel?
                parallel_map(n_samples) { |n| @classes[decision_values[n, true].max_index] }
              else
                Array.new(n_samples) { |n| @classes[decision_values[n, true].max_index] }
              end
  Numo::Int32.asarray(predicted)
end

#predict_proba(x) ⇒ Numo::DFloat

Predict probability for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the probailities.

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_classes]) Predicted probability of each class per sample.



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# File 'lib/rumale/polynomial_model/factorization_machine_classifier.rb', line 153

def predict_proba(x)
  check_sample_array(x)
  proba = 1.0 / (Numo::NMath.exp(-decision_function(x)) + 1.0)
  return (proba.transpose / proba.sum(axis: 1)).transpose if @classes.size > 2

  n_samples, = x.shape
  probs = Numo::DFloat.zeros(n_samples, 2)
  probs[true, 1] = proba
  probs[true, 0] = 1.0 - proba
  probs
end