Class: Rumale::Ensemble::GradientBoostingClassifier

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

Overview

GradientBoostingClassifier is a class that implements gradient tree boosting for classification. The class use negative binomial log-likelihood for the loss function. For multiclass classification problem, it uses one-vs-the-rest strategy.

reference

  • J H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, 29 (5), pp. 1189–1232, 2001.

  • J H. Friedman, “Stochastic Gradient Boosting,” Computational Statistics and Data Analysis, 38 (4), pp. 367–378, 2002.

    1. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proc. KDD’16, pp. 785–794, 2016.

Examples:

estimator =
  Rumale::Ensemble::GradientBoostingClassifier.new(
    n_estimators: 100, learning_rate: 0.3, reg_lambda: 0.001, 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::Classifier

#score

Constructor Details

#initialize(n_estimators: 100, learning_rate: 0.1, reg_lambda: 0.0, subsample: 1.0, max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, n_jobs: nil, random_seed: nil) ⇒ GradientBoostingClassifier

Create a new classifier with gradient tree boosting.

Parameters:

  • n_estimators (Integer) (defaults to: 100) —

    The numeber of trees for contructing classifier.

  • learning_rate (Float) (defaults to: 0.1) —

    The boosting learining rate

  • reg_lambda (Float) (defaults to: 0.0) —

    The L2 regularization term on weight.

  • 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.

  • 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. It is used to randomly determine the order of features when deciding spliting point.



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# File 'lib/rumale/ensemble/gradient_boosting_classifier.rb', line 65

def initialize(n_estimators: 100, learning_rate: 0.1, reg_lambda: 0.0, subsample: 1.0,
               max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1,
               max_features: nil, n_jobs: nil, random_seed: nil)
  check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                    max_features: max_features, n_jobs: n_jobs, random_seed: random_seed)
  check_params_integer(n_estimators: n_estimators, min_samples_leaf: min_samples_leaf)
  check_params_float(learning_rate: learning_rate, reg_lambda: reg_lambda, subsample: subsample)
  check_params_positive(n_estimators: n_estimators, learning_rate: learning_rate, reg_lambda: reg_lambda,
                        subsample: subsample, 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[:learning_rate] = learning_rate
  @params[:reg_lambda] = reg_lambda
  @params[:subsample] = subsample
  @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[:n_jobs] = n_jobs
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @estimators = nil
  @classes = nil
  @base_predictions = 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/rumale/ensemble/gradient_boosting_classifier.rb', line 36

def classes
  @classes
end

#estimators ⇒ Array<GradientTreeRegressor> (readonly)

Return the set of estimators.

Returns:

  • (Array<GradientTreeRegressor>) —

    or [Array<Array<GradientTreeRegressor>>]



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# File 'lib/rumale/ensemble/gradient_boosting_classifier.rb', line 32

def estimators
  @estimators
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature. The feature importances are calculated based on the numbers of times the feature is used for splitting.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



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# File 'lib/rumale/ensemble/gradient_boosting_classifier.rb', line 41

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/rumale/ensemble/gradient_boosting_classifier.rb', line 45

def rng
  @rng
end

Instance Method Details

#apply(x) ⇒ Numo::Int32

Return the index of the leaf that each sample reached.

Parameters:

  • x (Numo::DFloat) —

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

Returns:

  • (Numo::Int32) —

    (shape: [n_samples, n_estimators, n_classes]) Leaf index for sample.



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# File 'lib/rumale/ensemble/gradient_boosting_classifier.rb', line 176

def apply(x)
  check_sample_array(x)
  n_classes = @classes.size
  leaf_ids = if n_classes > 2
               Array.new(n_classes) { |n| @estimators[n].map { |tree| tree.apply(x) } }
             else
               @estimators.map { |tree| tree.apply(x) }
             end
  Numo::Int32[*leaf_ids].transpose
end

#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/rumale/ensemble/gradient_boosting_classifier.rb', line 133

def decision_function(x)
  check_sample_array(x)
  n_classes = @classes.size
  if n_classes > 2
    multiclass_scores(x)
  else
    @estimators.map { |tree| tree.predict(x) }.reduce(&:+) + @base_predictions
  end
end

#fit(x, y) ⇒ GradientBoostingClassifier

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/ensemble/gradient_boosting_classifier.rb', line 99

def fit(x, y)
  check_sample_array(x)
  check_label_array(y)
  check_sample_label_size(x, y)
  # initialize some variables.
  n_features = x.shape[1]
  @params[:max_features] = n_features if @params[:max_features].nil?
  @params[:max_features] = [[1, @params[:max_features]].max, n_features].min
  @classes = Numo::Int32[*y.to_a.uniq.sort]
  n_classes = @classes.size
  # train estimator.
  if n_classes > 2
    @base_predictions = multiclass_base_predictions(y)
    @estimators = multiclass_estimators(x, y)
  else
    negative_label = y.to_a.uniq.min
    bin_y = Numo::DFloat.cast(y.ne(negative_label)) * 2 - 1
    y_mean = bin_y.mean
    @base_predictions = 0.5 * Numo::NMath.log((1.0 + y_mean) / (1.0 - y_mean))
    @estimators = partial_fit(x, bin_y, @base_predictions)
  end
  # calculate feature importances.
  @feature_importances = if n_classes > 2
                           multiclass_feature_importances
                         else
                           @estimators.map(&:feature_importances).reduce(&:+)
                         end
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about GradientBoostingClassifier.



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# File 'lib/rumale/ensemble/gradient_boosting_classifier.rb', line 189

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

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/ensemble/gradient_boosting_classifier.rb', line 200

def marshal_load(obj)
  @params = obj[:params]
  @estimators = obj[:estimators]
  @classes = obj[:classes]
  @base_predictions = obj[:base_predictions]
  @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/rumale/ensemble/gradient_boosting_classifier.rb', line 147

def predict(x)
  check_sample_array(x)
  n_samples = x.shape[0]
  probs = predict_proba(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/rumale/ensemble/gradient_boosting_classifier.rb', line 158

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