Removed FNN because it requires wrapper as the knn.reg and knn methods do not use x,y arguments. Excluded neuralnet to focus on liquidSVM.

Author: stevenpawley

R/mlp_data.R

@@ -311,125 +311,3 @@ set_pred(
       )
   )
 )
-
-# ------------------------------------------------------------------------------
-
-set_model_engine("mlp", "classification", "neuralnet")
-set_model_engine("mlp", "regression", "neuralnet")
-set_dependency("mlp", "neuralnet", "neuralnet")
-
-set_model_arg(
-  model = "mlp",
-  eng = "neuralnet",
-  parsnip = "hidden_units",
-  original = "hidden",
-  func = list(pkg = "dials", fun = "hidden_units"),
-  has_submodel = FALSE
-)
-set_fit(
-  model = "mlp",
-  eng = "neuralnet",
-  mode = "classification",
-  value = list(
-    interface = "formula",
-    protect = c("formula", "data"),
-    func = c(pkg = "neuralnet", fun = "neuralnet"),
-    defaults = list(
-      rep = 1,
-      linear.output = FALSE
-    )
-  )
-)
-set_fit(
-  model = "mlp",
-  eng = "neuralnet",
-  mode = "regression",
-  value = list(
-    interface = "formula",
-    protect = c("formula", "data"),
-    func = c(pkg = "neuralnet", fun = "neuralnet"),
-    defaults = list(
-      rep = 1,
-      linear.output = TRUE
-    )
-  )
-)
-set_pred(
-  model = "mlp",
-  eng = "neuralnet",
-  mode = "classification",
-  type = "class",
-  value = list(
-    pre = NULL,
-    post = function(x, object) object$lvl[apply(x, 1, which.max)],
-    func = c(pkg = "stats", fun = "predict"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data)
-    )
-  )
-)
-set_pred(
-  model = "mlp",
-  eng = "neuralnet",
-  mode = "classification",
-  type = "prob",
-  value = list(
-    pre = NULL,
-    post = function(x, object) {
-      colnames(x) <- object$lvl
-      tibble::as_tibble(x)
-    },
-    func = c(pkg = "stats", fun = "predict"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data),
-      prob = TRUE
-    )
-  )
-)
-set_pred(
-  model = "mlp",
-  eng = "neuralnet",
-  mode = "classification",
-  type = "raw",
-  value = list(
-    pre = NULL,
-    post = NULL,
-    func = c(pkg = "stats", fun = "predict"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data)
-    )
-  )
-)
-set_pred(
-  model = "mlp",
-  eng = "neuralnet",
-  mode = "regression",
-  type = "numeric",
-  value = list(
-    pre = NULL,
-    post = function(x, object) as.numeric(x),
-    func = c(pkg = "stats", fun = "predict"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data)
-    )
-  )
-)
-set_pred(
-  model = "mlp",
-  eng = "neuralnet",
-  mode = "regression",
-  type = "raw",
-  value = list(
-    pre = NULL,
-    post = NULL,
-    func = c(pkg = "stats", fun = "predict"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data)
-    )
-  )
-)

R/nearest_neighbor.R

@@ -211,86 +211,3 @@ knn_by_k <- function(k, object, new_data, type, ...) {
     dplyr::mutate(neighbors = k, .row = dplyr::row_number()) %>%
     dplyr::select(.row, neighbors, dplyr::starts_with(".pred"))
 }
-
-# ------------------------------------------------------------------------------
-
-#' Nearest neighbors using FNN
-#'
-#' `fnn_train` is a wrapper for `FNN` fast nearest neighbor models
-#'
-#' @param x a data frame or matrix of predictors.
-#' @param y a vector (factor or numeric) or matrix (numeric) of outcome data.
-#' @param k a vector (integer) of the number of neighbours to consider.
-#' @param algorithm character, one of c("kd_tree", "cover_tree", "brute"),
-#'   default = "kd_tree"
-#' @param ... additional arguments to pass to FNN, currently unused.
-#'
-#' @return list containing the FNN call
-#' @export
-fnn_train <- function(x, y = NULL, k = 1, algorithm = "kd_tree", ...) {
-
-  # regression
-  if (is.numeric(y)) {
-    fun <- "knn.reg"
-    main_args <- list(
-      train = rlang::enquo(x),
-      y = rlang::enquo(y),
-      k = k,
-      algorithm = algorithm)
-    call <- parsnip:::make_call(fun = fun, ns = "FNN", main_args)
-    rlang::eval_tidy(call, env = rlang::current_env())
-
-    # for classification return unevaluated call because FNN:knn
-    # trains and predicts in same call
-  } else {
-    fun <- "knn"
-    main_args <- list(
-      train = rlang::enquo(x),
-      cl = rlang::enquo(y),
-      k = k,
-      algorithm = algorithm)
-    call <- parsnip:::make_call(fun = fun, ns = "FNN", main_args)
-    list(call = call)
-  }
-}
-
-
-#' Nearest neighbors prediction using FNN
-#'
-#' `fnn_pred` is a wrapper for `FNN` fast nearest neighbor models
-#'
-#' @param object parsnip model spec.
-#' @param newdata data.frame or matrix of training data.
-#' @param prob logical return predicted probability of the winning class,
-#'   default = FALSE.
-#' @param ... additional arguments to pass to FNN, currently unused.
-#'
-#' @return data.frame containing the predicted results.
-#' @export
-fnn_pred <- function(object, newdata, prob = FALSE, ...) {
-
-  # modify the call for prediction
-  object$call$test <- newdata
-
-  # regression result
-  if ("y" %in% names(object$call)) {
-    res <- rlang::eval_tidy(object$call)
-    res <- res$pred
-
-    # classification result
-  } else {
-    object$call$prob <- prob
-    lvl <- levels(rlang::eval_tidy(object$call$cl))
-    res <- rlang::eval_tidy(object$call)
-
-    # probability for winning class
-    if (prob == FALSE) {
-      attributes(res) <- NULL
-      res <- factor(lvl[res], levels = lvl)
-    } else {
-      res <- attr(res, "prob")
-    }
-  }
-
-  res
-}

R/nearest_neighbor_data.R

@@ -172,116 +172,3 @@ set_pred(
       )
   )
 )
-
-# ------------------------------------------------------------------------------
-
-set_model_engine("nearest_neighbor", "classification", "FNN")
-set_model_engine("nearest_neighbor", "regression", "FNN")
-set_dependency("nearest_neighbor", "FNN", "FNN")
-
-set_model_arg(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  parsnip = "neighbors",
-  original = "k",
-  func = list(pkg = "dials", fun = "neighbors"),
-  has_submodel = FALSE
-)
-set_fit(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  mode = "regression",
-  value = list(
-    interface = "matrix",
-    protect = c("x", "y"),
-    func = c(fun = "fnn_train"),
-    defaults = list()
-  )
-)
-set_fit(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  mode = "classification",
-  value = list(
-    interface = "matrix",
-    protect = c("x", "y"),
-    func = c(fun = "fnn_train"),
-    defaults = list()
-  )
-)
-set_pred(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  mode = "regression",
-  type = "numeric",
-  value = list(
-    pre = NULL,
-    post = NULL,
-    func = c(fun = "fnn_pred"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data)
-    )
-  )
-)
-set_pred(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  mode = "regression",
-  type = "raw",
-  value = list(
-    pre = NULL,
-    post = NULL,
-    func = c(fun = "fnn_pred"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data)
-    )
-  )
-)
-set_pred(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  mode = "classification",
-  type = "class",
-  value = list(
-    pre = NULL,
-    post = NULL,
-    func = c(fun = "fnn_pred"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data)
-    )
-  )
-)
-set_pred(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  mode = "classification",
-  type = "prob",
-  value = list(
-    pre = NULL,
-    post = function(result, object) tibble::as_tibble(result),
-    func = c(fun = "fnn_pred"),
-    args =
-      list(
-        object = quote(object$fit),
-        newdata = quote(new_data),
-        prob = TRUE
-      )
-  )
-)
-set_pred(
-  model = "nearest_neighbor",
-  eng = "FNN",
-  mode = "classification",
-  type = "raw",
-  value = list(
-    pre = NULL,
-    post = NULL,
-    func = c(fun = "fnn_pred"),
-    args = list(
-      object = quote(object$fit),
-      newdata = quote(new_data))
-  )
-)