Class: SVMKit::LinearModel::PegasosSVC

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Classifier
Defined in:
lib/svmkit/linear_model/pegasos_svc.rb

Overview

PegasosSVC is a class that implements Support Vector Classifier with the Pegasos algorithm.

estimator =
  SVMKit::LinearModel::PegasosSVC.new(reg_param: 1.0, max_iter: 100, batch_size: 20, random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)

  • Reference:

      1. Shalev-Shwartz and Y. Singer, “Pegasos: Primal Estimated sub-GrAdient SOlver for SVM,” Proc. ICML’07, pp. 807–814, 2007.

Constant Summary collapse

DEFAULT_PARAMS =

:nodoc:

{ # :nodoc:
  reg_param: 1.0,
  max_iter: 100,
  batch_size: 50,
  random_seed: nil
}.freeze

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Constructor Details

#initialize(params = {}) ⇒ PegasosSVC

Create a new classifier with Support Vector Machine by the Pegasos algorithm.

:call-seq:

new(reg_param: 1.0, max_iter: 100, batch_size: 50, random_seed: 1) -> PegasosSVC

  • Arguments :

    • :reg_param (Float) (defaults to: 1.0) – The regularization parameter.

    • :max_iter (Integer) (defaults to: 100) – The maximum number of iterations.

    • :batch_size (Integer) (defaults to: 50) – The size of the mini batches.

    • :random_seed (Integer) (defaults to: nil) – The seed value using to initialize the random generator.



44
45
46
47
48
49
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 44

def initialize(params = {})
  self.params = DEFAULT_PARAMS.merge(Hash[params.map { |k, v| [k.to_sym, v] }])
  self.params[:random_seed] ||= srand
  @weight_vec = nil
  @rng = Random.new(self.params[:random_seed])
end

Instance Attribute Details

#rng ⇒ Object (readonly)

The random generator for performing random sampling in the Pegasos algorithm.



32
33
34
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 32

def rng
  @rng
end

#weight_vec ⇒ Object (readonly)

The weight vector for SVC.



29
30
31
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 29

def weight_vec
  @weight_vec
end

Instance Method Details

#decision_function(x) ⇒ Object

Calculate confidence scores for samples.

:call-seq:

decision_function(x) -> NMatrix, shape: [1, n_samples]

  • Arguments :

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

  • Returns :

    • Confidence score per sample.



101
102
103
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 101

def decision_function(x)
  @weight_vec.dot(x.transpose)
end

#fit(x, y) ⇒ Object

Fit the model with given training data.

:call-seq:

fit(x, y) -> PegasosSVC

  • Arguments :

    • x (NMatrix, shape: [n_samples, n_features]) – The training data to be used for fitting the model.

    • y (NMatrix, shape: [1, n_samples]) – The labels to be used for fitting the model.

  • Returns :

    • The learned classifier itself.



61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 61

def fit(x, y)
  # Generate binary labels
  negative_label = y.uniq.sort.shift
  bin_y = y.to_flat_a.map { |l| l != negative_label ? 1 : -1 }
  # Initialize some variables.
  n_samples, n_features = x.shape
  rand_ids = [*0..n_samples - 1].shuffle(random: @rng)
  @weight_vec = NMatrix.zeros([1, n_features])
  # Start optimization.
  params[:max_iter].times do |t|
    # random sampling
    subset_ids = rand_ids.shift(params[:batch_size])
    rand_ids.concat(subset_ids)
    target_ids = subset_ids.map do |n|
      n if @weight_vec.dot(x.row(n).transpose) * bin_y[n] < 1
    end
    n_subsamples = target_ids.size
    next if n_subsamples.zero?
    # update the weight vector.
    eta = 1.0 / (params[:reg_param] * (t + 1))
    mean_vec = NMatrix.zeros([1, n_features])
    target_ids.each { |n| mean_vec += x.row(n) * bin_y[n] }
    mean_vec *= eta / n_subsamples
    @weight_vec = @weight_vec * (1.0 - eta * params[:reg_param]) + mean_vec
    # scale the weight vector.
    scaler = (1.0 / params[:reg_param]**0.5) / @weight_vec.norm2
    @weight_vec *= [1.0, scaler].min
  end
  self
end

#marshal_dump ⇒ Object

Serializes object through Marshal#dump.



135
136
137
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 135

def marshal_dump # :nodoc:
  { params: params, weight_vec: Utils.dump_nmatrix(@weight_vec), rng: @rng }
end

#marshal_load(obj) ⇒ Object

Deserialize object through Marshal#load.



140
141
142
143
144
145
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 140

def marshal_load(obj) # :nodoc:
  self.params = obj[:params]
  @weight_vec = Utils.restore_nmatrix(obj[:weight_vec])
  @rng = obj[:rng]
  nil
end

#predict(x) ⇒ Object

Predict class labels for samples.

:call-seq:

predict(x) -> NMatrix, shape: [1, n_samples]

  • Arguments :

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

  • Returns :

    • Predicted class label per sample.



114
115
116
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 114

def predict(x)
  decision_function(x).map { |v| v >= 0 ? 1 : -1 }
end

#score(x, y) ⇒ Object

Claculate the mean accuracy of the given testing data.

:call-seq:

predict(x, y) -> Float

  • Arguments :

    • x (NMatrix, shape: [n_samples, n_features]) – Testing data.

    • y (NMatrix, shape: [1, n_samples]) – True labels for testing data.

  • Returns :

    • Mean accuracy



128
129
130
131
132
 
# File 'lib/svmkit/linear_model/pegasos_svc.rb', line 128

def score(x, y)
  p = predict(x)
  n_hits = (y.to_flat_a.map.with_index { |l, n| l == p[n] ? 1 : 0 }).inject(:+)
  n_hits / y.size.to_f
end