Module: XGBoost

Defined in:
lib/xgboost.rb,
lib/xgboost/ffi.rb,
lib/xgboost/model.rb,
lib/xgboost/utils.rb,
lib/xgboost/ranker.rb,
lib/xgboost/booster.rb,
lib/xgboost/cv_pack.rb,
lib/xgboost/dmatrix.rb,
lib/xgboost/version.rb,
lib/xgboost/regressor.rb,
lib/xgboost/classifier.rb,
lib/xgboost/early_stopping.rb,
lib/xgboost/packed_booster.rb,
lib/xgboost/training_callback.rb,
lib/xgboost/callback_container.rb,
lib/xgboost/evaluation_monitor.rb

Defined Under Namespace

Modules: FFI, Utils Classes: Booster, CVPack, CallbackContainer, Classifier, DMatrix, EarlyStopping, Error, EvaluationMonitor, Model, PackedBooster, Ranker, Regressor, TrainingCallback

Constant Summary collapse

VERSION = 
"0.10.0"

Class Attribute Summary collapse

Class Method Summary collapse

Class Attribute Details

.ffi_lib ⇒ Object

Returns the value of attribute ffi_lib.



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

def ffi_lib
  @ffi_lib
end

Class Method Details

.cv(params, dtrain, num_boost_round: 10, nfold: 3, maximize: nil, early_stopping_rounds: nil, verbose_eval: nil, show_stdv: true, seed: 0, callbacks: nil, shuffle: true) ⇒ Object 



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# File 'lib/xgboost.rb', line 97

def cv(
  params,
  dtrain,
  num_boost_round: 10,
  nfold: 3,
  maximize: nil,
  early_stopping_rounds: nil,
  verbose_eval: nil,
  show_stdv: true,
  seed: 0,
  callbacks: nil,
  shuffle: true
)
  results = {}
  cvfolds =
    mknfold(
      dall: dtrain,
      param: params,
      nfold: nfold,
      seed: seed,
      shuffle: shuffle
    )

  callbacks = callbacks.nil? ? [] : callbacks.dup

  if verbose_eval
    verbose_eval = verbose_eval == true ? 1 : verbose_eval
    callbacks << EvaluationMonitor.new(period: verbose_eval, show_stdv: show_stdv)
  end
  if early_stopping_rounds
    callbacks << EarlyStopping.new(rounds: early_stopping_rounds, maximize: maximize)
  end
  callbacks_container = CallbackContainer.new(callbacks, is_cv: true)

  booster = PackedBooster.new(cvfolds)
  callbacks_container.before_training(booster)

  num_boost_round.times do |i|
    break if callbacks_container.before_iteration(booster, i, dtrain, nil)
    booster.update(i)

    should_break = callbacks_container.after_iteration(booster, i, dtrain, nil)
    res = callbacks_container.aggregated_cv
    res.each do |key, mean, std|
      if !results.include?(key + "-mean")
        results[key + "-mean"] = []
      end
      if !results.include?(key + "-std")
        results[key + "-std"] = []
      end
      results[key + "-mean"] << mean
      results[key + "-std"] << std
    end

    if should_break
      results.keys.each do |k|
        results[k] = results[k][..booster.best_iteration]
      end
      break
    end
  end

  callbacks_container.after_training(booster)

  results
end

.lib_version ⇒ Object 



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# File 'lib/xgboost.rb', line 164

def lib_version
  major = ::FFI::MemoryPointer.new(:int)
  minor = ::FFI::MemoryPointer.new(:int)
  patch = ::FFI::MemoryPointer.new(:int)
  FFI.XGBoostVersion(major, minor, patch)
  "#{major.read_int}.#{minor.read_int}.#{patch.read_int}"
end

.train(params, dtrain, num_boost_round: 10, evals: nil, maximize: nil, early_stopping_rounds: nil, evals_result: nil, verbose_eval: true, callbacks: nil) ⇒ Object 



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# File 'lib/xgboost.rb', line 55

def train(
  params,
  dtrain,
  num_boost_round: 10,
  evals: nil,
  maximize: nil,
  early_stopping_rounds: nil,
  evals_result: nil,
  verbose_eval: true,
  callbacks: nil
)
  callbacks = callbacks.nil? ? [] : callbacks.dup
  evals ||= []

  bst = Booster.new(params: params, cache: [dtrain] + evals.map { |d| d[0] })

  if verbose_eval
    verbose_eval = verbose_eval == true ? 1 : verbose_eval
    callbacks << EvaluationMonitor.new(period: verbose_eval)
  end
  if early_stopping_rounds
    callbacks << EarlyStopping.new(rounds: early_stopping_rounds, maximize: maximize)
  end
  cb_container = CallbackContainer.new(callbacks)

  bst = cb_container.before_training(bst)

  num_boost_round.times do |i|
    break if cb_container.before_iteration(bst, i, dtrain, evals)
    bst.update(dtrain, i)
    break if cb_container.after_iteration(bst, i, dtrain, evals)
  end

  bst = cb_container.after_training(bst)

  if !evals_result.nil?
    evals_result.merge!(cb_container.history)
  end

  bst.reset
end