Class: Rumale::KernelMachine::KernelSVC

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Classifier
Defined in:
lib/rumale/kernel_machine/kernel_svc.rb

Overview

KernelSVC is a class that implements (Nonlinear) Kernel Support Vector Classifier with stochastic gradient descent (SGD) optimization. For multiclass classification problem, it uses one-vs-the-rest strategy.

Reference

    1. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter, “Pegasos: Primal Estimated sub-GrAdient SOlver for SVM,” Mathematical Programming, vol. 127 (1), pp. 3–30, 2011.

Examples:

training_kernel_matrix = Rumale::PairwiseMetric::rbf_kernel(training_samples)
estimator =
  Rumale::KernelMachine::KernelSVC.new(reg_param: 1.0, max_iter: 1000, random_seed: 1)
estimator.fit(training_kernel_matrix, traininig_labels)
testing_kernel_matrix = Rumale::PairwiseMetric::rbf_kernel(testing_samples, training_samples)
results = estimator.predict(testing_kernel_matrix)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Classifier

#score

Constructor Details

#initialize(reg_param: 1.0, max_iter: 1000, probability: false, random_seed: nil) ⇒ KernelSVC

Create a new classifier with Kernel Support Vector Machine by the SGD optimization.

Parameters:

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

    The regularization parameter.

  • max_iter (Integer) (defaults to: 1000) —

    The maximum number of iterations.

  • probability (Boolean) (defaults to: false) —

    The flag indicating whether to perform probability estimation.

  • random_seed (Integer) (defaults to: nil) —

    The seed value using to initialize the random generator.



46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 46

def initialize(reg_param: 1.0, max_iter: 1000, probability: false, random_seed: nil)
  check_params_float(reg_param: reg_param)
  check_params_integer(max_iter: max_iter)
  check_params_boolean(probability: probability)
  check_params_type_or_nil(Integer, random_seed: random_seed)
  check_params_positive(reg_param: reg_param, max_iter: max_iter)
  @params = {}
  @params[:reg_param] = reg_param
  @params[:max_iter] = max_iter
  @params[:probability] = probability
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @weight_vec = nil
  @prob_param = nil
  @classes = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_classes])



34
35
36
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 34

def classes
  @classes
end

#rng ⇒ Random (readonly)

Return the random generator for performing random sampling.

Returns:

  • (Random) 


38
39
40
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 38

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector for Kernel SVC.

Returns:

  • (Numo::DFloat) —

    (shape: [n_classes, n_trainig_sample])



30
31
32
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 30

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_testing_samples, n_training_samples]) The kernel matrix between testing samples and training samples to compute the scores.

Returns:

  • (Numo::DFloat) —

    (shape: [n_testing_samples, n_classes]) Confidence score per sample.



110
111
112
113
114
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 110

def decision_function(x)
  check_sample_array(x)

  x.dot(@weight_vec.transpose)
end

#fit(x, y) ⇒ KernelSVC

Fit the model with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_training_samples, n_training_samples]) The kernel matrix of the training data to be used for fitting the model.

  • y (Numo::Int32) —

    (shape: [n_training_samples]) The labels to be used for fitting the model.

Returns:

  • (KernelSVC) —

    The learned classifier itself.



70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 70

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
    @weight_vec = Numo::DFloat.zeros(n_classes, n_features)
    @prob_param = Numo::DFloat.zeros(n_classes, 2)
    n_classes.times do |n|
      bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
      @weight_vec[n, true] = binary_fit(x, bin_y)
      @prob_param[n, true] = if @params[:probability]
                               Rumale::ProbabilisticOutput.fit_sigmoid(x.dot(@weight_vec[n, true].transpose), bin_y)
                             else
                               Numo::DFloat[1, 0]
                             end
    end
  else
    negative_label = y.to_a.uniq.min
    bin_y = Numo::Int32.cast(y.ne(negative_label)) * 2 - 1
    @weight_vec = binary_fit(x, bin_y)
    @prob_param = if @params[:probability]
                    Rumale::ProbabilisticOutput.fit_sigmoid(x.dot(@weight_vec.transpose), bin_y)
                  else
                    Numo::DFloat[1, 0]
                  end
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about KernelSVC.



153
154
155
156
157
158
159
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 153

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

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


163
164
165
166
167
168
169
170
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 163

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

#predict(x) ⇒ Numo::Int32

Predict class labels for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_testing_samples, n_training_samples]) The kernel matrix between testing samples and training samples to predict the labels.

Returns:

  • (Numo::Int32) —

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



121
122
123
124
125
126
127
128
129
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 121

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
  decision_values = decision_function(x)
  Numo::Int32.asarray(Array.new(n_samples) { |n| @classes[decision_values[n, true].max_index] })
end

#predict_proba(x) ⇒ Numo::DFloat

Predict probability for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_testing_samples, n_training_samples]) The kernel matrix between testing samples and training samples to predict the labels.

Returns:

  • (Numo::DFloat) —

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



136
137
138
139
140
141
142
143
144
145
146
147
148
149
 
# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 136

def predict_proba(x)
  check_sample_array(x)

  if @classes.size > 2
    probs = 1.0 / (Numo::NMath.exp(@prob_param[true, 0] * decision_function(x) + @prob_param[true, 1]) + 1.0)
    return (probs.transpose / probs.sum(axis: 1)).transpose
  end

  n_samples, = x.shape
  probs = Numo::DFloat.zeros(n_samples, 2)
  probs[true, 1] = 1.0 / (Numo::NMath.exp(@prob_param[0] * decision_function(x) + @prob_param[1]) + 1.0)
  probs[true, 0] = 1.0 - probs[true, 1]
  probs
end