Class: SVMKit::Ensemble::AdaBoostClassifier

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

Overview

AdaBoostClassifier is a class that implements AdaBoost (SAMME.R) for classification. This class uses decision tree for a weak learner.

Reference

    1. Zhu, S. Rosset, H. Zou, and T.Hashie, “Multi-class AdaBoost,” Technical Report No. 430, Department of Statistics, University of Michigan, 2005.

Examples:

estimator =
  SVMKit::Ensemble::AdaBoostClassifier.new(
    n_estimators: 10, criterion: 'gini', max_depth: 3, max_leaf_nodes: 10, min_samples_leaf: 5, 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_estimators: 50, criterion: 'gini', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ AdaBoostClassifier

Create a new classifier with AdaBoost.

Parameters:

  • n_estimators (Integer) (defaults to: 50) —

    The numeber of decision trees for contructing random forest.

  • criterion (String) (defaults to: 'gini') —

    The function to evalue spliting point. Supported criteria are ‘gini’ and ‘entropy’.

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

    The maximum depth of the tree. If nil is given, decision tree grows without concern for depth.

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

    The maximum number of leaves on decision tree. If nil is given, number of leaves is not limited.

  • min_samples_leaf (Integer) (defaults to: 1) —

    The minimum number of samples at a leaf node.

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

    The number of features to consider when searching optimal split point. If nil is given, split process considers all features.

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

    The seed value using to initialize the random generator. It is used to randomly determine the order of features when deciding spliting point.



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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 55

def initialize(n_estimators: 50, criterion: 'gini', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1,
               max_features: nil, random_seed: nil)
  SVMKit::Validation.check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                                       max_features: max_features, random_seed: random_seed)
  SVMKit::Validation.check_params_integer(n_estimators: n_estimators, min_samples_leaf: min_samples_leaf)
  SVMKit::Validation.check_params_string(criterion: criterion)
  SVMKit::Validation.check_params_positive(n_estimators: n_estimators, max_depth: max_depth,
                                           max_leaf_nodes: max_leaf_nodes, min_samples_leaf: min_samples_leaf,
                                           max_features: max_features)
  @params = {}
  @params[:n_estimators] = n_estimators
  @params[:criterion] = criterion
  @params[:max_depth] = max_depth
  @params[:max_leaf_nodes] = max_leaf_nodes
  @params[:min_samples_leaf] = min_samples_leaf
  @params[:max_features] = max_features
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @estimators = nil
  @classes = nil
  @feature_importances = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (size: n_classes)



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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 32

def classes
  @classes
end

#estimators ⇒ Array<DecisionTreeClassifier> (readonly)

Return the set of estimators.

Returns:

  • (Array<DecisionTreeClassifier>) 


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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 28

def estimators
  @estimators
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 36

def feature_importances
  @feature_importances
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.

Returns:

  • (Random) 


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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 40

def rng
  @rng
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, n_classes]) Confidence score per sample.



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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 136

def decision_function(x)
  SVMKit::Validation.check_sample_array(x)
  n_samples, = x.shape
  n_classes = @classes.size
  sum_probs = Numo::DFloat.zeros(n_samples, n_classes)
  @estimators.each do |tree|
    log_proba = Numo::NMath.log(tree.predict_proba(x).clip(1.0e-15, nil))
    sum_probs += (n_classes - 1) * (log_proba - 1.fdiv(n_classes) * Numo::DFloat[log_proba.sum(1)].transpose)
  end
  sum_probs /= @estimators.size
end

#fit(x, y) ⇒ AdaBoostClassifier

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/svmkit/ensemble/ada_boost_classifier.rb', line 84

def fit(x, y) # rubocop:disable Metrics/AbcSize
  SVMKit::Validation.check_sample_array(x)
  SVMKit::Validation.check_label_array(y)
  SVMKit::Validation.check_sample_label_size(x, y)
  ## Initialize some variables.
  n_samples, n_features = x.shape
  @estimators = []
  @feature_importances = Numo::DFloat.zeros(n_features)
  @params[:max_features] = n_features unless @params[:max_features].is_a?(Integer)
  @params[:max_features] = [[1, @params[:max_features]].max, n_features].min
  @classes = Numo::Int32.asarray(y.to_a.uniq.sort)
  n_classes = @classes.shape[0]
  ## Boosting.
  classes_arr = @classes.to_a
  y_codes = Numo::DFloat.zeros(n_samples, n_classes) - 1.fdiv(n_classes - 1)
  n_samples.times { |n| y_codes[n, classes_arr.index(y[n])] = 1.0 }
  observation_weights = Numo::DFloat.zeros(n_samples) + 1.fdiv(n_samples)
  @params[:n_estimators].times do |_t|
    # Fit classfier.
    ids = weighted_sampling(observation_weights)
    break if y[ids].to_a.uniq.size != n_classes
    tree = Tree::DecisionTreeClassifier.new(
      criterion: @params[:criterion], max_depth: @params[:max_depth],
      max_leaf_nodes: @params[:max_leaf_nodes], min_samples_leaf: @params[:min_samples_leaf],
      max_features: @params[:max_features], random_seed: @rng.rand(int_max)
    )
    tree.fit(x[ids, true], y[ids])
    # Calculate estimator error.
    proba = tree.predict_proba(x).clip(1.0e-15, nil)
    p = Numo::Int32.asarray(Array.new(n_samples) { |n| @classes[proba[n, true].max_index] })
    inds = p.ne(y)
    error = (observation_weights * inds).sum / observation_weights.sum
    # Store model.
    @estimators.push(tree)
    @feature_importances += tree.feature_importances
    break if error.zero?
    # Update observation weights.
    log_proba = Numo::NMath.log(proba)
    observation_weights *= Numo::NMath.exp(-1.0 * (n_classes - 1).fdiv(n_classes) * (y_codes * log_proba).sum(1))
    observation_weights = observation_weights.clip(1.0e-15, nil)
    sum_observation_weights = observation_weights.sum
    break if sum_observation_weights.zero?
    observation_weights /= sum_observation_weights
  end
  @feature_importances /= @feature_importances.sum
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about AdaBoostClassifier.



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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 174

def marshal_dump
  { params: @params, estimators: @estimators, classes: @classes,
    feature_importances: @feature_importances, rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/svmkit/ensemble/ada_boost_classifier.rb', line 181

def marshal_load(obj)
  @params = obj[:params]
  @estimators = obj[:estimators]
  @classes = obj[:classes]
  @feature_importances = obj[:feature_importances]
  @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/svmkit/ensemble/ada_boost_classifier.rb', line 152

def predict(x)
  SVMKit::Validation.check_sample_array(x)
  n_samples, = x.shape
  probs = decision_function(x)
  Numo::Int32.asarray(Array.new(n_samples) { |n| @classes[probs[n, true].max_index] })
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/svmkit/ensemble/ada_boost_classifier.rb', line 163

def predict_proba(x)
  SVMKit::Validation.check_sample_array(x)
  n_classes = @classes.size
  probs = Numo::NMath.exp(1.fdiv(n_classes - 1) * decision_function(x))
  sum_probs = probs.sum(1)
  probs /= Numo::DFloat[sum_probs].transpose
  probs
end