Module: EasyML::Core::Models::XGBoostCore

Included in:
XGBoost, Models::XGBoost
Defined in:
lib/easy_ml/core/models/xgboost_core.rb

Constant Summary collapse

OBJECTIVES = 
{
  classification: {
    binary: %w[binary:logistic binary:hinge],
    multi_class: %w[multi:softmax multi:softprob]
  },
  regression: %w[reg:squarederror reg:logistic]
}

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Instance Attribute Details

#booster ⇒ Object

Returns the value of attribute booster.



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 41

def booster
  @booster
end

#model ⇒ Object

Returns the value of attribute model.



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 41

def model
  @model
end

Class Method Details

.included(base) ⇒ Object 



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 14

def self.included(base)
  base.class_eval do
    attribute :evaluator

    dependency :callbacks, { array: true } do |dep|
      dep.option :wandb do |opt|
        opt.set_class Wandb::XGBoostCallback
        opt.bind_attribute :log_model, default: false
        opt.bind_attribute :log_feature_importance, default: true
        opt.bind_attribute :importance_type, default: "gain"
        opt.bind_attribute :define_metric, default: true
        opt.bind_attribute :project_name
      end
    end

    dependency :hyperparameters do |dep|
      dep.set_class EasyML::Models::Hyperparameters::XGBoost
      dep.bind_attribute :batch_size, default: 32
      dep.bind_attribute :learning_rate, default: 1.1
      dep.bind_attribute :max_depth, default: 6
      dep.bind_attribute :n_estimators, default: 100
      dep.bind_attribute :booster, default: "gbtree"
      dep.bind_attribute :objective, default: "reg:squarederror"
    end
  end
end

Instance Method Details

#_save_model_file(path) ⇒ Object 



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 80

def _save_model_file(path)
  puts "XGBoost received path #{path}"
  @booster.save_model(path)
end

#base_model ⇒ Object 



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 89

def base_model
  ::XGBoost
end

#customize_callbacks {|callbacks| ... } ⇒ Object

Yields:

  • (callbacks) 


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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 93

def customize_callbacks
  yield callbacks
end

#feature_importances ⇒ Object 



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 85

def feature_importances
  @model.booster.feature_names.zip(@model.feature_importances).to_h
end

#load(path = nil) ⇒ Object 



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 69

def load(path = nil)
  path ||= file
  path = path&.file&.file if path.class.ancestors.include?(CarrierWave::Uploader::Base)

  raise "No existing model at #{path}" unless File.exist?(path)

  initialize_model do
    booster_class.new(params: hyperparameters.to_h, model_file: path)
  end
end

#predict(xs) ⇒ Object 



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 43

def predict(xs)
  raise "No trained model! Train a model before calling predict" unless @booster.present?
  raise "Cannot predict on nil — XGBoost" if xs.nil?

  y_pred = @booster.predict(preprocess(xs))

  case task.to_sym
  when :classification
    to_classification(y_pred)
  else
    y_pred
  end
end

#predict_proba(data) ⇒ Object 



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# File 'lib/easy_ml/core/models/xgboost_core.rb', line 57

def predict_proba(data)
  dmat = DMatrix.new(data)
  y_pred = @booster.predict(dmat)

  if y_pred.first.is_a?(Array)
    # multiple classes
    y_pred
  else
    y_pred.map { |v| [1 - v, v] }
  end
end