Predict and new models

DESCRIPTION

@@ -17,7 +17,7 @@ Depends:
     R (>= 2.10)
 Imports: 
     dplyr,
-    rlang (>= 0.2.0.9001),
+    rlang (>=  0.3.0.1),
     purrr,
     utils,
     tibble,
@@ -38,6 +38,4 @@ Suggests:
     C50,
     xgboost,
     covr
-Remotes:
-    tidyverse/rlang,
-    r-lib/generics
+

NAMESPACE

@@ -19,8 +19,8 @@ S3method(predict_classprob,"_lognet")
 S3method(predict_classprob,"_multnet")
 S3method(predict_classprob,model_fit)
 S3method(predict_confint,model_fit)
-S3method(predict_num,"_elnet")
-S3method(predict_num,model_fit)
+S3method(predict_numeric,"_elnet")
+S3method(predict_numeric,model_fit)
 S3method(predict_predint,model_fit)
 S3method(predict_quantile,model_fit)
 S3method(predict_raw,"_elnet")
@@ -38,12 +38,16 @@ S3method(print,multinom_reg)
 S3method(print,nearest_neighbor)
 S3method(print,rand_forest)
 S3method(print,surv_reg)
+S3method(print,svm_poly)
+S3method(print,svm_rbf)
 S3method(translate,boost_tree)
 S3method(translate,default)
 S3method(translate,mars)
 S3method(translate,mlp)
 S3method(translate,rand_forest)
 S3method(translate,surv_reg)
+S3method(translate,svm_poly)
+S3method(translate,svm_rbf)
 S3method(type_sum,model_fit)
 S3method(type_sum,model_spec)
 S3method(update,boost_tree)
@@ -55,6 +59,8 @@ S3method(update,multinom_reg)
 S3method(update,nearest_neighbor)
 S3method(update,rand_forest)
 S3method(update,surv_reg)
+S3method(update,svm_poly)
+S3method(update,svm_rbf)
 S3method(varying_args,model_spec)
 S3method(varying_args,recipe)
 S3method(varying_args,step)
@@ -92,8 +98,8 @@ export(predict_classprob)
 export(predict_classprob.model_fit)
 export(predict_confint)
 export(predict_confint.model_fit)
-export(predict_num)
-export(predict_num.model_fit)
+export(predict_numeric)
+export(predict_numeric.model_fit)
 export(predict_predint)
 export(predict_predint.model_fit)
 export(predict_quantile)
@@ -106,6 +112,8 @@ export(set_engine)
 export(set_mode)
 export(show_call)
 export(surv_reg)
+export(svm_poly)
+export(svm_rbf)
 export(translate)
 export(varying)
 export(varying_args)

R/boost_tree.R

@@ -359,6 +359,9 @@ xgb_pred <- function(object, newdata, ...) {
 #' @export
 multi_predict._xgb.Booster <-
   function(object, new_data, type = NULL, trees = NULL, ...) {
+    if (any(names(enquos(...)) == "newdata"))
+      stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
+
     if (is.null(trees))
       trees <- object$fit$nIter
     trees <- sort(trees)
@@ -388,10 +391,10 @@ xgb_by_tree <- function(tree, object, new_data, type, ...) {
     nms <- names(pred)
   } else {
     if (type == "class") {
-      pred <- boost_tree_xgboost_data$classes$post(pred, object)
+      pred <- boost_tree_xgboost_data$class$post(pred, object)
       pred <- tibble(.pred = factor(pred, levels = object$lvl))
     } else {
-      pred <- boost_tree_xgboost_data$prob$post(pred, object)
+      pred <- boost_tree_xgboost_data$classprob$post(pred, object)
       pred <- as_tibble(pred)
       names(pred) <- paste0(".pred_", names(pred))
     }
@@ -458,6 +461,9 @@ C5.0_train <-
 #' @export
 multi_predict._C5.0 <-
   function(object, new_data, type = NULL, trees = NULL, ...) {
+    if (any(names(enquos(...)) == "newdata"))
+      stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
+
     if (is.null(trees))
       trees <- min(object$fit$trials)
     trees <- sort(trees)

R/boost_tree_data.R

@@ -31,7 +31,7 @@ boost_tree_xgboost_data <-
           verbose = 0
         )
     ),
-    pred = list(
+    numeric = list(
       pre = NULL,
       post = NULL,
       func = c(fun = "xgb_pred"),
@@ -41,7 +41,7 @@ boost_tree_xgboost_data <-
           newdata = quote(new_data)
         )
     ),
-    classes = list(
+    class = list(
       pre = NULL,
       post = function(x, object) {
         if (is.vector(x)) {
@@ -58,7 +58,7 @@ boost_tree_xgboost_data <-
           newdata = quote(new_data)
         )
     ),
-    prob = list(
+    classprob = list(
       pre = NULL,
       post = function(x, object) {
         if (is.vector(x)) {
@@ -97,7 +97,7 @@ boost_tree_C5.0_data <-
       func = c(pkg = "parsnip", fun = "C5.0_train"),
       defaults = list()
     ),
-    classes = list(
+    class = list(
       pre = NULL,
       post = NULL,
       func = c(fun = "predict"),
@@ -106,7 +106,7 @@ boost_tree_C5.0_data <-
         newdata = quote(new_data)
       )
     ),
-    prob = list(
+    classprob = list(
       pre = NULL,
       post = function(x, object) {
         as_tibble(x)
@@ -142,7 +142,7 @@ boost_tree_spark_data <-
           seed = expr(sample.int(10^5, 1))
         )
     ),
-    pred = list(
+    numeric = list(
       pre = NULL,
       post = format_spark_num,
       func = c(pkg = "sparklyr", fun = "ml_predict"),
@@ -152,7 +152,7 @@ boost_tree_spark_data <-
           dataset = quote(new_data)
         )
     ),
-    classes = list(
+    class = list(
       pre = NULL,
       post = format_spark_class,
       func = c(pkg = "sparklyr", fun = "ml_predict"),
@@ -162,7 +162,7 @@ boost_tree_spark_data <-
           dataset = quote(new_data)
         )
     ),
-    prob = list(
+    classprob = list(
       pre = NULL,
       post = format_spark_probs,
       func = c(pkg = "sparklyr", fun = "ml_predict"),

R/linear_reg.R

@@ -2,11 +2,11 @@
 #'
 #' `linear_reg` is a way to generate a _specification_ of a model
 #'  before fitting and allows the model to be created using
-#'  different packages in R, Stan, or via Spark. The main arguments for the
-#'  model are:
+#'  different packages in R, Stan, keras, or via Spark. The main
+#'  arguments for the model are:
 #' \itemize{
 #'   \item \code{penalty}: The total amount of regularization
-#'  in the model. Note that this must be zero for some engines .
+#'  in the model. Note that this must be zero for some engines.
 #'   \item \code{mixture}: The proportion of L1 regularization in
 #'  the model. Note that this will be ignored for some engines.
 #' }
@@ -19,8 +19,11 @@
 #' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  The only possible value for this model is "regression".
-#' @param penalty An non-negative number representing the
-#'  total amount of regularization (`glmnet` and `spark` only).
+#' @param penalty An non-negative number representing the total
+#'  amount of regularization (`glmnet`, `keras`, and `spark` only).
+#'  For `keras` models, this corresponds to purely L2 regularization
+#'  (aka weight decay) while the other models can be a combination
+#'  of L1 and L2 (depending on the value of `mixture`).
 #' @param mixture A number between zero and one (inclusive) that
 #'  represents the proportion of regularization that is used for the
 #'  L2 penalty (i.e. weight decay, or ridge regression) versus L1
@@ -36,6 +39,7 @@
 #' \item \pkg{R}:  `"lm"` or `"glmnet"`
 #' \item \pkg{Stan}:  `"stan"`
 #' \item \pkg{Spark}: `"spark"`
+#' \item \pkg{keras}: `"keras"`
 #' }
 #'
 #' @section Engine Details:
@@ -59,6 +63,10 @@
 #' \pkg{spark}
 #'
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "spark")}
+#' 
+#' \pkg{keras}
+#'
+#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::linear_reg(), "keras")}
 #'
 #' When using `glmnet` models, there is the option to pass
 #'  multiple values (or no values) to the `penalty` argument.
@@ -211,18 +219,27 @@ organize_glmnet_pred <- function(x, object) {
 #' @export
 predict._elnet <-
   function(object, new_data, type = NULL, opts = list(), ...) {
+    if (any(names(enquos(...)) == "newdata"))
+      stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
+
     object$spec <- eval_args(object$spec)
     predict.model_fit(object, new_data = new_data, type = type, opts = opts, ...)
   }
 
 #' @export
-predict_num._elnet <- function(object, new_data, ...) {
+predict_numeric._elnet <- function(object, new_data, ...) {
+  if (any(names(enquos(...)) == "newdata"))
+    stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
+
   object$spec <- eval_args(object$spec)
-  predict_num.model_fit(object, new_data = new_data, ...)
+  predict_numeric.model_fit(object, new_data = new_data, ...)
 }
 
 #' @export
 predict_raw._elnet <- function(object, new_data, opts = list(), ...)  {
+  if (any(names(enquos(...)) == "newdata"))
+    stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
+
   object$spec <- eval_args(object$spec)
   predict_raw.model_fit(object, new_data = new_data, opts = opts, ...)
 }
@@ -232,6 +249,9 @@ predict_raw._elnet <- function(object, new_data, opts = list(), ...)  {
 #' @export
 multi_predict._elnet <-
   function(object, new_data, type = NULL, penalty = NULL, ...) {
+    if (any(names(enquos(...)) == "newdata"))
+      stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
+
     dots <- list(...)
     if (is.null(penalty))
       penalty <- object$fit$lambda

R/linear_reg_data.R

@@ -4,17 +4,19 @@ linear_reg_arg_key <- data.frame(
   glmnet =  c(   "lambda",             "alpha"),
   spark  =  c("reg_param", "elastic_net_param"),
   stan   =  c(        NA,                  NA),
+  keras  =  c(   "decay",                  NA),
   stringsAsFactors = FALSE,
   row.names =  c("penalty", "mixture")
 )
 
 linear_reg_modes <- "regression"
 
 linear_reg_engines <- data.frame(
-  lm    = TRUE,
+  lm     = TRUE,
   glmnet = TRUE,
   spark  = TRUE,
   stan   = TRUE,
+  keras  = TRUE,
   row.names =  c("regression")
 )
 
@@ -30,7 +32,7 @@ linear_reg_lm_data <-
       func = c(pkg = "stats", fun = "lm"),
       defaults = list()
     ),
-    pred = list(
+    numeric = list(
       pre = NULL,
       post = NULL,
       func = c(fun = "predict"),
@@ -100,7 +102,7 @@ linear_reg_glmnet_data <-
           family = "gaussian"
         )
     ),
-    pred = list(
+    numeric = list(
       pre = NULL,
       post = organize_glmnet_pred,
       func = c(fun = "predict"),
@@ -135,7 +137,7 @@ linear_reg_stan_data <-
           family = expr(stats::gaussian)
         )
     ),
-    pred = list(
+    numeric = list(
       pre = NULL,
       post = NULL,
       func = c(fun = "predict"),
@@ -224,7 +226,7 @@ linear_reg_spark_data <-
       protect = c("x", "formula", "weight_col"),
       func = c(pkg = "sparklyr", fun = "ml_linear_regression")
     ),
-    pred = list(
+    numeric = list(
       pre = NULL,
       post = function(results, object) {
         results <- dplyr::rename(results, pred = prediction)
@@ -240,5 +242,24 @@ linear_reg_spark_data <-
     )
   )
 
-
+linear_reg_keras_data <-
+  list(
+    libs = c("keras", "magrittr"),
+    fit = list(
+      interface = "matrix",
+      protect = c("x", "y"),
+      func = c(pkg = "parsnip", fun = "keras_mlp"),
+      defaults = list(hidden_units = 1, act = "linear")
+    ),
+    numeric = list(
+      pre = NULL,
+      post = maybe_multivariate,
+      func = c(fun = "predict"),
+      args =
+        list(
+          object = quote(object$fit),
+          x = quote(as.matrix(new_data))
+        )
+    )
+  )