R/SL.ksvm.R

Defines functions predict.SL.ksvm SL.ksvm

Documented in predict.SL.ksvm SL.ksvm

#' @title Wrapper for Kernlab's SVM algorithm
#'
#' @description Wrapper for Kernlab's support vector machine algorithm.
#'
#' @param Y Outcome variable
#' @param X Training dataframe
#' @param newX Test dataframe
#' @param family Gaussian or binomial
#' @param type ksvm can be used for classification , for regression, or for
#'   novelty detection. Depending on whether y is a factor or not, the default
#'   setting for type is C-svc or eps-svr, respectively, but can be overwritten
#'   by setting an explicit value. See ?ksvm for more details.
#' @param kernel the kernel function used in training and predicting. This
#'   parameter can be set to any function, of class kernel, which computes the
#'   inner product in feature space between two vector arguments. See ?ksvm for
#'   more details.
#' @param kpar the list of hyper-parameters (kernel parameters). This is a list
#'   which contains the parameters to be used with the kernel function. See
#'   ?ksvm for more details.
#' @param scaled A logical vector indicating the variables to be scaled. If
#'   scaled is of length 1, the value is recycled as many times as needed and
#'   all non-binary variables are scaled. Per default, data are scaled
#'   internally (both x and y variables) to zero mean and unit variance. The
#'   center and scale values are returned and used for later predictions.
#' @param C cost of constraints violation (default: 1) this is the 'C'-constant
#'   of the regularization term in the Lagrange formulation.
#' @param nu parameter needed for nu-svc, one-svc, and nu-svr. The nu parameter
#'   sets the upper bound on the training error and the lower bound on the
#'   fraction of data points to become Support Vectors (default: 0.2).
#' @param epsilon epsilon in the insensitive-loss function used for eps-svr,
#'   nu-svr and eps-bsvm (default: 0.1)
#' @param cross if a integer value k>0 is specified, a k-fold cross validation
#'   on the training data is performed to assess the quality of the model: the
#'   accuracy rate for classification and the Mean Squared Error for regression
#' @param prob.model if set to TRUE builds a model for calculating class
#'   probabilities or in case of regression, calculates the scaling parameter of
#'   the Laplacian distribution fitted on the residuals. Fitting is done on
#'   output data created by performing a 3-fold cross-validation on the training
#'   data. (default: FALSE)
#' @param class.weights a named vector of weights for the different classes,
#'   used for asymmetric class sizes. Not all factor levels have to be supplied
#'   (default weight: 1). All components have to be named.
#' @param cache cache memory in MB (default 40)
#' @param tol tolerance of termination criterion (default: 0.001)
#' @param shrinking option whether to use the shrinking-heuristics (default: TRUE)
#' @param ... Any additional parameters, not currently passed through.
#'
#' @return List with predictions and the original training data &
#'   hyperparameters.
#'
#' @references
#'
#' Hsu, C. W., Chang, C. C., & Lin, C. J. (2016). A practical guide to support
#' vector classification. \url{http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf}
#'
#' Scholkopf, B., & Smola, A. J. (2001). Learning with kernels: support vector
#' machines, regularization, optimization, and beyond. MIT press.
#'
#' Vapnik, V. N. (1998). Statistical learning theory (Vol. 1). New York: Wiley.
#'
#' Zeileis, A., Hornik, K., Smola, A., & Karatzoglou, A. (2004). kernlab-an S4
#' package for kernel methods in R. Journal of statistical software, 11(9),
#' 1-20.
#'
#' @examples
#'
#' data(Boston, package = "MASS")
#' Y = Boston$medv
#' # Remove outcome from covariate dataframe.
#' X = Boston[, -14]
#'
#' set.seed(1)
#'
#' sl = SuperLearner(Y, X, family = gaussian(),
#'                  SL.library = c("SL.mean", "SL.ksvm"))
#' sl
#'
#' pred = predict(sl, X)
#' summary(pred$pred)
#'
#' @importFrom kernlab predict
#'
#' @seealso \code{\link{predict.SL.ksvm}} \code{\link[kernlab]{ksvm}}
#'   \code{\link[kernlab]{predict.ksvm}}
#'
#' @encoding utf-8
#'
#' @export
SL.ksvm = function(Y, X, newX, family,
                   type = NULL,
                   kernel = "rbfdot",
                   kpar = "automatic",
                   scaled = T,
                   C = 1,
                   nu = 0.2,
                   epsilon = 0.1,
                   cross = 0,
                   prob.model = family$family == "binomial",
                   class.weights = NULL,
                   cache = 40,
                   tol = 0.001,
                   shrinking = T,
                   ...) {
  .SL.require("kernlab")

  # Make sure X is a matrix rather than a dataframe.
  if (!is.matrix(X)) {
    X = model.matrix(~ ., data = X)
    # Remove intercept column.
    X = X[, -1]
  }

  if (family$family == "binomial") {
    Y = as.factor(Y)
    predict_type = "probabilities"
  } else {
    predict_type = "response"
  }

  model = kernlab::ksvm(X, Y,
                        scaled = scaled,
                        type = type,
                        kernel = kernel,
                        kpar = kpar,
                        C = C,
                        nu = nu,
                        epsilon = epsilon,
                        prob.model = prob.model,
                        class.weights = class.weights)

  # Make sure newX is a matrix rather than a dataframe.
  if (!is.matrix(newX)) {
    newX = model.matrix(~ ., data = newX)
    # Remove intercept column.
    newX = newX[, -1, drop = FALSE]
  }

  # CK: I could not get this to work using simply predict(). Not sure why.
  pred = kernlab::predict(model, newX, predict_type)

  if (family$family == "binomial") {
    # Second column is P(Y = 1 | X).
    pred = pred[, 2]
  }

  fit = list(object = model, family = family)

  out = list(pred = pred, fit = fit)

  class(out$fit) = "SL.ksvm"
  return(out)
}

#' Prediction for SL.ksvm
#'
#' @param object SL.kernlab object
#' @param newdata Dataframe to generate predictions
#' @param family Gaussian or binomial
#' @param coupler Coupling method used in the multiclass case, can be one of
#'   minpair or pkpd (see kernlab package for details). For future usage.
#' @param ... Unused additional arguments
#'
#' @importFrom kernlab predict
#'
#' @seealso \code{\link{SL.ksvm}} \code{\link[kernlab]{ksvm}} \code{\link[kernlab]{predict.ksvm}}
#'
#' @export
predict.SL.ksvm <- function(object, newdata, family, coupler = "minpair", ...) {
  .SL.require("kernlab")

  # Make sure X is a matrix rather than a dataframe.
  if (!is.matrix(newdata)) {
    newdata = model.matrix(~ ., data = newdata)
    # Remove intercept column.
    newdata = newdata[, -1, drop = FALSE]
  }

  if (family$family == "binomial") {
    predict_type = "probabilities"
  } else {
    predict_type = "response"
  }

  # CK: I could not get this to work using simply predict(). Not sure why.
  pred = kernlab::predict(object$object, newdata, predict_type, coupler = coupler)

  if (family$family == "binomial") {
    # Second column is P(Y = 1 | X).
    pred = pred[, 2]
  }

  return(pred)
}

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SuperLearner documentation built on May 10, 2021, 9:10 a.m.