R/RLearner_regr_cvglmnet.R

In Najah-lshanableh/R-data-mining2: Machine Learning in R

#' @export
makeRLearner.regr.cvglmnet = function() {
  makeRLearnerRegr(
    cl = "regr.cvglmnet",
    package = "glmnet",
    par.set = makeParamSet(
      makeDiscreteLearnerParam(id = "family", default = "gaussian", values = c("gaussian", "poisson")),
      makeNumericLearnerParam(id = "alpha", default = 1, lower = 0, upper = 1),
      makeIntegerLearnerParam(id = "nfolds", default = 10L, lower = 3L),
      makeDiscreteLearnerParam(id = "type.measure", values = c("mse", "mae"), default = "mse"),
      makeDiscreteLearnerParam(id = "s", values = c("lambda.1se", "lambda.min"), default = "lambda.1se", when = "predict"),
      makeIntegerLearnerParam(id = "nlambda", default = 100L, lower = 1L),
      makeNumericLearnerParam(id = "lambda.min.ratio", lower = 0, upper = 1),
      makeLogicalLearnerParam(id = "standardize", default = TRUE),
      makeLogicalLearnerParam(id = "intercept", default = TRUE),
      makeNumericLearnerParam(id = "thresh", default = 1e-07, lower = 0),
      makeIntegerLearnerParam(id = "dfmax", lower = 0L),
      makeIntegerLearnerParam(id = "pmax", lower = 0L),
      makeIntegerVectorLearnerParam(id = "exclude", lower = 1L),
      makeNumericVectorLearnerParam(id = "penalty.factor", lower = 0, upper = 1),
      makeNumericVectorLearnerParam(id = "lower.limits", upper = 0),
      makeNumericVectorLearnerParam(id = "upper.limits", lower = 0),
      makeIntegerLearnerParam(id = "maxit", default = 100000L, lower = 1L),
      #FIXME Data dependent default. If n.features < 500 'covariance', 'naive' otherwise
      makeDiscreteLearnerParam(id = "type.gaussian", values = c("covariance", "naive")),
      makeNumericLearnerParam(id = "fdev", default = 1.0e-5, lower = 0, upper = 1),
      makeNumericLearnerParam(id = "devmax", default = 0.999, lower = 0, upper = 1),
      makeNumericLearnerParam(id = "eps", default = 1.0e-6, lower = 0, upper = 1),
      makeNumericLearnerParam(id = "big", default = 9.9e35),
      makeIntegerLearnerParam(id = "mnlam", default = 5, lower = 1),
      makeNumericLearnerParam(id = "pmin", default = 1.0e-9, lower = 0, upper = 1),
      makeNumericLearnerParam(id = "exmx", default = 250.0),
      makeNumericLearnerParam(id = "prec", default = 1e-10),
      makeIntegerLearnerParam(id = "mxit", default = 100L, lower = 1L)
    ),
    properties = c("numerics", "factors", "weights"),
    name = "GLM with Lasso or Elasticnet Regularization (Cross Validated Lambda)",
    short.name = "cvglmnet",
    note = "Factors automatically get converted to dummy columns, ordered factors to integer.
    glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults
    before setting the specified parameters and after training.
    If you are setting glmnet.control parameters through glmnet.control,
    you need to save and re-set them after running the glmnet learner.",
    callees = c("cv.glmnet", "glmnet", "glmnet.control", "predict.cv.glmnet")
  )
}

#' @export
trainLearner.regr.cvglmnet = function(.learner, .task, .subset, .weights = NULL, ...) {
  d = getTaskData(.task, .subset, target.extra = TRUE)
  info = getFixDataInfo(d$data, factors.to.dummies = TRUE, ordered.to.int = TRUE)
  args = c(list(x = as.matrix(fixDataForLearner(d$data, info)), y = d$target), list(...))
  rm(d)
  if (!is.null(.weights))
    args$weights = .weights

  glmnet::glmnet.control(factory = TRUE)
  saved.ctrl = glmnet::glmnet.control()
  is.ctrl.arg = names(args) %in% names(saved.ctrl)
  if (any(is.ctrl.arg)) {
    on.exit(glmnet::glmnet.control(factory = TRUE))
    do.call(glmnet::glmnet.control, args[is.ctrl.arg])
    args = args[!is.ctrl.arg]
  }

  attachTrainingInfo(do.call(glmnet::cv.glmnet, args), info)
}

#' @export
predictLearner.regr.cvglmnet = function(.learner, .model, .newdata, ...) {
  info = getTrainingInfo(.model)
  .newdata = as.matrix(fixDataForLearner(.newdata, info))
  p = drop(predict(.model$learner.model, newx = .newdata, type = "response", ...))
  p
}