Class: XGBoost::EarlyStopping

Inherits:
TrainingCallback show all
Defined in:
lib/xgboost/early_stopping.rb

Instance Method Summary collapse

Methods inherited from TrainingCallback

#before_iteration

Constructor Details

#initialize(rounds:, metric_name: nil, data_name: nil, maximize: nil, save_best: false, min_delta: 0.0) ⇒ EarlyStopping

Returns a new instance of EarlyStopping.



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# File 'lib/xgboost/early_stopping.rb', line 3

def initialize(
  rounds:,
  metric_name: nil,
  data_name: nil,
  maximize: nil,
  save_best: false,
  min_delta: 0.0
)
  @data = data_name
  @metric_name = metric_name
  @rounds = rounds
  @save_best = save_best
  @maximize = maximize
  @stopping_history = {}
  @min_delta = min_delta
  if @min_delta < 0
    raise ArgumentError, "min_delta must be greater or equal to 0."
  end

  @current_rounds = 0
  @best_scores = {}
  @starting_round = 0
  super()
end

Instance Method Details

#after_iteration(model, epoch, evals_log) ⇒ Object 



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# File 'lib/xgboost/early_stopping.rb', line 33

def after_iteration(model, epoch, evals_log)
  epoch += @starting_round
  msg = "Must have at least 1 validation dataset for early stopping."
  if evals_log.keys.length < 1
    raise ArgumentError, msg
  end

  # Get data name
  if @data
    data_name = @data
  else
    # Use the last one as default.
    data_name = evals_log.keys[-1]
  end
  if !evals_log.include?(data_name)
    raise ArgumentError, "No dataset named: #{data_name}"
  end

  if !data_name.is_a?(String)
    raise TypeError, "The name of the dataset should be a string. Got: #{data_name.class.name}"
  end
  data_log = evals_log[data_name]

  # Get metric name
  if @metric_name
    metric_name = @metric_name
  else
    # Use last metric by default.
    metric_name = data_log.keys[-1]
  end
  if !data_log.include?(metric_name)
    raise ArgumentError, "No metric named: #{metric_name}"
  end

  # The latest score
  score = data_log[metric_name][-1]
  update_rounds(
    score, data_name, metric_name, model, epoch
  )
end

#after_training(model) ⇒ Object 



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# File 'lib/xgboost/early_stopping.rb', line 74

def after_training(model)
  if !@save_best
    return model
  end

  best_iteration = model.best_iteration
  best_score = model.best_score
  # model = model[..(best_iteration + 1)]
  model.best_iteration = best_iteration
  model.best_score = best_score
  model
end

#before_training(model) ⇒ Object 



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# File 'lib/xgboost/early_stopping.rb', line 28

def before_training(model)
  @starting_round = model.num_boosted_rounds
  model
end