Misc updates

.travis.yml

@@ -15,9 +15,15 @@ r:
 - devel
 
 env:
-  - KERAS_BACKEND="tensorflow"
   global:
-    - MAKEFLAGS="-j 2"
+  - KERAS_BACKEND="tensorflow"
+  - MAKEFLAGS="-j 2"
+
+# until we troubleshoot these issues
+matrix:
+  allow_failures:
+    - r: 3.1
+    - r: 3.2
 
 r_binary_packages: 
  - rstan

NAMESPACE

@@ -22,6 +22,7 @@ S3method(predict_confint,model_fit)
 S3method(predict_num,"_elnet")
 S3method(predict_num,model_fit)
 S3method(predict_predint,model_fit)
+S3method(predict_quantile,model_fit)
 S3method(predict_raw,"_elnet")
 S3method(predict_raw,"_lognet")
 S3method(predict_raw,"_multnet")
@@ -95,6 +96,8 @@ export(predict_num)
 export(predict_num.model_fit)
 export(predict_predint)
 export(predict_predint.model_fit)
+export(predict_quantile)
+export(predict_quantile.model_fit)
 export(predict_raw)
 export(predict_raw.model_fit)
 export(rand_forest)
@@ -113,10 +116,12 @@ import(rlang)
 importFrom(dplyr,arrange)
 importFrom(dplyr,as_tibble)
 importFrom(dplyr,bind_cols)
+importFrom(dplyr,bind_rows)
 importFrom(dplyr,collect)
 importFrom(dplyr,full_join)
 importFrom(dplyr,funs)
 importFrom(dplyr,group_by)
+importFrom(dplyr,mutate)
 importFrom(dplyr,pull)
 importFrom(dplyr,rename)
 importFrom(dplyr,rename_at)
@@ -159,6 +164,7 @@ importFrom(stats,predict)
 importFrom(stats,qnorm)
 importFrom(stats,qt)
 importFrom(stats,quantile)
+importFrom(stats,setNames)
 importFrom(stats,terms)
 importFrom(stats,update)
 importFrom(tibble,as_tibble)

R/aaa_spark_helpers.R

@@ -3,12 +3,10 @@
 #' @importFrom dplyr starts_with rename rename_at vars funs
 format_spark_probs <- function(results, object) {
   results <- dplyr::select(results, starts_with("probability_"))
-  results <- dplyr::rename_at(
-    results,
-    vars(starts_with("probability_")),
-    funs(gsub("probability", "pred", .))
-  )
-  results
+  p <- ncol(results)
+  lvl <- paste0("probability_", 0:(p - 1))
+  names(lvl) <- paste0("pred_", object$fit$.index_labels)
+  results %>% rename(!!!syms(lvl))
 }
 
 format_spark_class <- function(results, object) {

R/fit_helpers.R

@@ -8,10 +8,14 @@ form_form <-
   function(object, control, env, ...) {
     opts <- quos(...)
 
-    y_levels <- levels_from_formula( # prob rewrite this as simple subset/levels
-      env$formula,
-      env$data
-    )
+    if (object$mode != "regression") {
+      y_levels <- levels_from_formula( # prob rewrite this as simple subset/levels
+        env$formula,
+        env$data
+      )
+    } else {
+      y_levels <- NULL
+    }
 
     object <- check_mode(object, y_levels)
 

R/misc.R

@@ -178,3 +178,15 @@ check_args <- function(object) {
 check_args.default <- function(object) {
   invisible(object)
 }
+
+# ------------------------------------------------------------------------------
+
+# copied form recipes
+
+names0 <- function (num, prefix = "x") {
+  if (num < 1)
+    stop("`num` should be > 0", call. = FALSE)
+  ind <- format(1:num)
+  ind <- gsub(" ", "0", ind)
+  paste0(prefix, ind)
+}

R/predict.R

@@ -7,8 +7,8 @@
 #' @param object An object of class `model_fit`
 #' @param new_data A rectangular data object, such as a data frame.
 #' @param type A single character value or `NULL`. Possible values
-#'  are "numeric", "class", "probs", "conf_int", "pred_int", or
-#'  "raw". When `NULL`, `predict` will choose an appropriate value
+#'  are "numeric", "class", "probs", "conf_int", "pred_int", "quantile",
+#'  or "raw". When `NULL`, `predict` will choose an appropriate value
 #'  based on the model's mode.
 #' @param opts A list of optional arguments to the underlying
 #'  predict function that will be used when `type = "raw"`. The
@@ -45,6 +45,10 @@
 #'  produces for class probabilities (or other non-scalar outputs),
 #'  the columns will be named `.pred_lower_classlevel` and so on.
 #'
+#' Quantile predictions return a tibble with a column `.pred`, which is
+#'  a list-column. Each list element contains a tibble with columns
+#'  `.pred` and `.quantile` (and perhaps others).
+#'
 #' Using `type = "raw"` with `predict.model_fit` (or using
 #'  `predict_raw`) will return the unadulterated results of the
 #'  prediction function.
@@ -96,6 +100,7 @@ predict.model_fit <- function (object, new_data, type = NULL, opts = list(), ...
     prob     = predict_classprob(object = object, new_data = new_data, ...),
     conf_int = predict_confint(object = object, new_data = new_data, ...),
     pred_int = predict_predint(object = object, new_data = new_data, ...),
+    quantile = predict_quantile(object = object, new_data = new_data, ...),
     raw      = predict_raw(object = object, new_data = new_data, opts = opts, ...),
     stop("I don't know about type = '", "'", type, call. = FALSE)
   )
@@ -112,7 +117,8 @@ predict.model_fit <- function (object, new_data, type = NULL, opts = list(), ...
   res
 }
 
-pred_types <- c("raw", "numeric", "class", "link", "prob", "conf_int", "pred_int")
+pred_types <-
+  c("raw", "numeric", "class", "link", "prob", "conf_int", "pred_int", "quantile")
 
 #' @importFrom glue glue_collapse
 check_pred_type <- function(object, type) {

R/predict_quantile.R

@@ -0,0 +1,41 @@
+#' @keywords internal
+#' @rdname other_predict
+#' @param quant A vector of numbers between 0 and 1 for the quantile being 
+#'  predicted. 
+#' @inheritParams predict.model_fit
+#' @method predict_quantile model_fit
+#' @export predict_quantile.model_fit
+#' @export
+predict_quantile.model_fit <-
+  function (object, new_data, quantile = (1:9)/10, ...) {
+
+    if (is.null(object$spec$method$quantile))
+      stop("No quantile prediction method defined for this ",
+           "engine.", call. = FALSE)
+
+    new_data <- prepare_data(object, new_data)
+
+    # preprocess data
+    if (!is.null(object$spec$method$quantile$pre))
+      new_data <- object$spec$method$quantile$pre(new_data, object)
+
+    # Pass some extra arguments to be used in post-processor
+    object$spec$method$quantile$args$p <- quantile
+    pred_call <- make_pred_call(object$spec$method$quantile)
+
+    res <- eval_tidy(pred_call)
+
+    # post-process the predictions
+    if(!is.null(object$spec$method$quantile$post)) {
+      res <- object$spec$method$quantile$post(res, object)
+    }
+
+    res
+  }
+
+#' @export
+#' @keywords internal
+#' @rdname other_predict
+#' @inheritParams predict.model_fit
+predict_quantile <- function (object, ...)
+  UseMethod("predict_quantile")

R/surv_reg.R

@@ -25,16 +25,39 @@
 #'  `strata` function cannot be used. To achieve the same effect,
 #'  the extra parameter roles can be used (as described above).
 #'
-#' The model can be created using the `fit()` function using the
-#'  following _engines_:
-#' \itemize{
-#' \item \pkg{R}:  `"flexsurv"`
-#' }
 #' @inheritParams boost_tree
 #' @param mode A single character string for the type of model.
 #'  The only possible value for this model is "regression".
 #' @param dist A character string for the outcome distribution. "weibull" is
 #'  the default.
+#' @details
+#' For `surv_reg`, the mode will always be "regression".
+#'
+#' The model can be created using the `fit()` function using the
+#'  following _engines_:
+#' \itemize{
+#' \item \pkg{R}:  `"flexsurv"`, `"survreg"`
+#' }
+#'
+#' @section Engine Details:
+#'
+#' Engines may have pre-set default arguments when executing the
+#'  model fit call. These can be changed by using the `...`
+#'  argument to pass in the preferred values. For this type of
+#'  model, the template of the fit calls are:
+#'
+#' \pkg{flexsurv}
+#'
+#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::surv_reg(), "flexsurv")}
+#'
+#' \pkg{survreg}
+#'
+#' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::surv_reg(), "survreg")}
+#'
+#' Note that `model = TRUE` is needed to produce quantile
+#'  predictions when there is a stratification variable and can be
+#'  overridden in other cases.
+#'
 #' @seealso [varying()], [fit()], [survival::Surv()]
 #' @references Jackson, C. (2016). `flexsurv`: A Platform for Parametric Survival
 #'  Modeling in R. _Journal of Statistical Software_, 70(8), 1 - 33.
@@ -160,3 +183,51 @@ check_args.surv_reg <- function(object) {
 
   invisible(object)
 }
+
+# ------------------------------------------------------------------------------
+
+#' @importFrom stats setNames
+#' @importFrom dplyr mutate
+survreg_quant <- function(results, object) {
+  pctl <- object$spec$method$quantile$args$p
+  n <- nrow(results)
+  p <- ncol(results)
+  results <-
+    results %>%
+    as_tibble() %>%
+    setNames(names0(p)) %>%
+    mutate(.row = 1:n) %>%
+    gather(.label, .pred, -.row) %>%
+    arrange(.row, .label) %>%
+    mutate(.quantile = rep(pctl, n)) %>%
+    dplyr::select(-.label)
+  .row <- results[[".row"]]
+  results <-
+    results %>%
+    dplyr::select(-.row)
+  results <- split(results, .row)
+  names(results) <- NULL
+  tibble(.pred = results)
+}
+
+# ------------------------------------------------------------------------------
+
+#' @importFrom dplyr bind_rows
+flexsurv_mean <- function(results, object) {
+  results <- unclass(results)
+  results <- bind_rows(results)
+  results$est
+}
+
+#' @importFrom stats setNames
+flexsurv_quant <- function(results, object) {
+  results <- map(results, as_tibble)
+  names(results) <- NULL
+  results <- map(results, setNames, c(".quantile", ".pred", ".pred_lower", ".pred_upper"))
+}
+
+# ------------------------------------------------------------------------------
+
+#' @importFrom utils globalVariables
+utils::globalVariables(".label")
+