restyle and fix args

R/Lrnr_caret.R

@@ -66,7 +66,6 @@ Lrnr_caret <- R6Class(
   ),
   private = list(
     .properties = c("continuous", "binomial", "categorical", "wrapper"),
-
     .train = function(task) {
       # set type
       outcome_type <- self$get_outcome_type(task)

R/Lrnr_hal9001.R

@@ -99,7 +99,6 @@ Lrnr_hal9001 <- R6Class(
 
       return(fit_object)
     },
-
     .predict = function(task = NULL) {
       predictions <- stats::predict(
         self$fit_object,
@@ -111,7 +110,6 @@ Lrnr_hal9001 <- R6Class(
       }
       return(predictions)
     },
-
     .required_packages = c("hal9001", "glmnet")
   )
 )

R/importance.R

@@ -1,17 +1,19 @@
 utils::globalVariables(c("score"))
+
 #' Variable Importance
 #'
-#' Function that takes a cross-validated fit (i.e., cross-validated learner that
-#' has already been trained on a task), which could be a cross-validated single
-#' learner or super learner, and generates a risk-based variable importance
-#' score for either each covariate or each group of covariates in the task.
-#' This function outputs a \code{data.table}, where each row corresponds to the
-#' risk difference or the risk ratio between the following two risks: the risk
-#' when a covariate (or group of covariates) is permuted or removed, and the
-#' original risk (i.e., when all covariates are included as they were in the
-#' observed data). A higher risk ratio/difference corresponds to a more
-#' important covariate/group. A plot can be generated from the returned
-#' \code{data.table} by calling companion function \code{\link{importance_plot}}.
+#' Function that takes a cross-validated fit (i.e., cross-validated learner
+#' that has already been trained on a task), which could be a cross-validated
+#' single learner or super learner, and generates a risk-based variable
+#' importance score for either each covariate or each group of covariates in
+#' the task.  This function outputs a \code{data.table}, where each row
+#' corresponds to the risk difference or the risk ratio between the following
+#' two risks: the risk when a covariate (or group of covariates) is permuted or
+#' removed, and the original risk (i.e., when all covariates are included as
+#' they were in the observed data). A higher risk ratio/difference corresponds
+#' to a more important covariate/group. A plot can be generated from the
+#' returned \code{data.table} by calling companion function
+#' \code{\link{importance_plot}}.
 #'
 #' @export
 #'
@@ -23,45 +25,42 @@ utils::globalVariables(c("score"))
 #'
 #' @return A \code{data.table} of variable importance for each covariate.
 #'
-#' @section Parameters:
-#'   - \code{fit}: A trained cross-validated (CV) learner (such as a CV stack
-#'       or super learner), from which cross-validated predictions can be
-#'       generated.
-#'   - \code{eval_fun = NULL}: The evaluation function (risk or loss function)
-#'       for evaluating the risk. Defaults vary based on the outcome type,
-#'       matching defaults in \code{\link{default_metalearner}}. See
-#'       \code{\link{loss_functions}} and \code{\link{risk_functions}} for
-#'       options.
-#'   - \code{fold_number}: The fold number to use for obtaining the predictions
-#'       from the fit. Either a positive integer for obtaining predictions from
-#'       a specific fold's fit; \code{"full"} for obtaining predictions from a
-#'       fit on all of the data, or \code{"validation"} (default) for obtaining
-#'       cross-validated predictions, where the data used for training and
-#'       prediction never overlaps across the folds. Note that if a positive
-#'       integer or \code{"full"} is supplied here then there will be overlap
-#'       between the data used for training and validation, so
-#'       \code{fold_number ="validation"} is recommended.
-#'   - \code{type}: Which method should be used to obscure the relationship
-#'       between each covariate / covariate group and the outcome? When
-#'       \code{type} is \code{"remove"} (default), each covariate / covariate
-#'       group is removed one at a time from the task; the cross-validated
-#'       learner is refit to this modified task; and finally, predictions are
-#'       obtained from this refit. When \code{type} is \code{"permute"}, each
-#'       covariate / covariate group is permuted (sampled without replacement)
-#'       one at a time, and then predictions are obtained from this modified
-#'       data.
-#'   - \code{importance_metric}: Either \code{"ratio"} or \code{"difference"}
-#'       (default). For each covariate / covariate group, \code{"ratio"}
-#'       returns the risk of the permuted/removed covariate / covariate group
-#'       divided by observed/original risk (i.e., the risk with all covariates
-#'       as they existed in the sample) and \code{"difference"} returns the
-#'       difference between the risk with the permuted/removed covariate /
-#'       covariate group and the observed risk.
-#'   - \code{covariate_groups}: Optional named list covariate groups which will
-#'       invoke variable importance evaluation at the group-level, by
-#'       removing/permuting all covariates in the same group together. If
-#'       covariates in the task are not specified in the list of groups, then
-#'       those covariates will be added as additional single-covariate groups.
+#' @param fit A trained cross-validated (CV) learner (such as a CV stack or
+#'  super learner), from which cross-validated predictions can be generated.
+#' @param eval_fun The evaluation function (risk or loss function) for
+#'  evaluating the risk. Defaults vary based on the outcome type, matching
+#'  defaults in \code{\link{default_metalearner}}. See
+#'  \code{\link{loss_functions}} and \code{\link{risk_functions}} for options.
+#'  Default is \code{NULL}.
+#' @param fold_number The fold number to use for obtaining the predictions from
+#'  the fit. Either a positive integer for obtaining predictions from a
+#'  specific fold's fit; \code{"full"} for obtaining predictions from a fit on
+#'  all of the data, or \code{"validation"} (default) for obtaining
+#'  cross-validated predictions, where the data used for training and
+#'  prediction never overlaps across the folds. Note that if a positive integer
+#'  or \code{"full"} is supplied here then there will be overlap between the
+#'  data used for training and validation, so \code{fold_number ="validation"}
+#'  is recommended.
+#' @param type Which method should be used to obscure the relationship between
+#'  each covariate / covariate group and the outcome? When \code{type} is
+#'  \code{"remove"} (default), each covariate / covariate group is removed one
+#'  at a time from the task; the cross-validated learner is refit to this
+#'  modified task; and finally, predictions are obtained from this refit. When
+#'  \code{type} is \code{"permute"}, each covariate / covariate group is
+#'  permuted (sampled without replacement) one at a time, and then predictions
+#'  are obtained from this modified data.
+#' @param importance_metric Either \code{"ratio"} or \code{"difference"}
+#'  (default). For each covariate / covariate group, \code{"ratio"} returns the
+#'  risk of the permuted/removed covariate / covariate group divided by
+#'  observed/original risk (i.e., the risk with all covariates as they existed
+#'  in the sample) and \code{"difference"} returns the difference between the
+#'  risk with the permuted/removed covariate / covariate group and the observed
+#'  risk.
+#' @param covariate_groups Optional named list covariate groups which will
+#'  invoke variable importance evaluation at the group-level, by
+#'  removing/permuting all covariates in the same group together. If covariates
+#'  in the task are not specified in the list of groups, then those covariates
+#'  will be added as additional single-covariate groups.
 #'
 #' @examples
 #' # define ML task
@@ -246,12 +245,11 @@ importance <- function(fit, eval_fun = NULL,
 
 #' Variable Importance Plot
 #'
-#' @section Parameters:
-#'   - \code{x}: The 2-column \code{data.table} returned by
-#'       \code{\link{importance}}, where the first column is the
-#'       covariate/groups and the second column is the importance score.
-#'   - \code{nvar}: The maximum number of predictors to be plotted. Defaults to
-#'       the minimum between 30 and the number of rows in \code{x}.
+#' @param x The two-column \code{data.table} returned by
+#'  \code{\link{importance}}, where the first column is the covariate/groups
+#'  and the second column is the importance score.
+#' @param nvar The maximum number of predictors to be plotted. Defaults to the
+#'  minimum between 30 and the number of rows in \code{x}.
 #'
 #' @return A \code{\link[ggplot2]{ggplot}} of variable importance.
 #'
@@ -284,7 +282,6 @@ importance <- function(fit, eval_fun = NULL,
 #' importance_result <- importance(sl_fit)
 #' importance_plot(importance_result)
 importance_plot <- function(x, nvar = min(30, nrow(x))) {
-
   # get the importance metric
   xlab <- colnames(x)[2]
 
@@ -294,7 +291,9 @@ importance_plot <- function(x, nvar = min(30, nrow(x))) {
   x <- x[1:(min(nvar, nrow(x))), ]
 
   # format for ggplot
-  d <- data.table::data.table(vars = factor(x[[1]], levels = x[[1]]), score = x[[2]])
+  d <- data.table::data.table(
+    vars = factor(x[[1]], levels = x[[1]]), score = x[[2]]
+  )
 
   ggplot2::ggplot(d, aes(x = vars, y = score)) +
     ggplot2::geom_point() +

R/loss_functions.R

@@ -1,3 +1,5 @@
+utils::globalVariables(c("id", "loss", "obs", "pred", "wts"))
+
 #' Loss Function Definitions
 #'
 #' Loss functions for use in evaluating learner fits.
@@ -97,7 +99,7 @@ risk <- function(pred, observed, loss = loss_squared_error, weights = NULL) {
   }
 
   # calculate risk
-  risk <- weighted.mean(losses, weights)
+  risk <- stats::weighted.mean(losses, weights)
   return(risk)
 }
 
@@ -113,30 +115,28 @@ risk <- function(pred, observed, loss = loss_squared_error, weights = NULL) {
 #'
 #' @name risk_functions
 #'
-#' @param pred A vector of predicted values.
-#' @param observed A vector of binary observed values.
 #' @param measure A character indicating which \code{ROCR} performance measure
-#' to use for evaluation. The \code{measure} must be either cutoff-dependent
-#' so a single value can be selected (e.g., "tpr"), or it's value is a scalar
-#' (e.g., "aucpr"). For more information, see \code{\link[ROCR]{performance}}.
+#'  to use for evaluation. The \code{measure} must be either cutoff-dependent
+#'  so a single value can be selected (e.g., "tpr"), or it's value is a scalar
+#'  (e.g., "aucpr"). For more information, see \code{\link[ROCR]{performance}}.
 #' @param cutoff A numeric value specifying the cutoff for choosing a single
-#' performance measure from the returned set. Only used for performance measures
-#' that are cutoff-dependent and default is 0.5. See
-#' \code{\link[ROCR]{performance}} for more detail.
+#'  performance measure from the returned set. Only used for performance
+#'  measures that are cutoff-dependent and default is 0.5. See
+#'  \code{\link[ROCR]{performance}} for more detail.
 #' @param name An optional character string for user to supply their desired
-#' name for the performance measure, which will be used for naming subsequent
-#' risk-related tables and metrics (e.g., \code{cv_risk} column names). When
-#' \code{name} is not supplied, the \code{measure} will be used for naming.
+#'  name for the performance measure, which will be used for naming subsequent
+#'  risk-related tables and metrics (e.g., \code{cv_risk} column names). When
+#'  \code{name} is not supplied, the \code{measure} will be used for naming.
 #' @param ... Optional arguments to specific \code{ROCR} performance
-#' measures. See \code{\link[ROCR]{performance}} for more detail.
+#'  measures. See \code{\link[ROCR]{performance}} for more detail.
 #'
 #' @rdname risk_functions
 #'
 #' @importFrom ROCR prediction performance
 #'
 #' @export
-#'
 custom_ROCR_risk <- function(measure, cutoff = 0.5, name = NULL, ...) {
+  # NOTE: arguments to factory-produced function goes undocumented
   function(pred, observed) {
 
     # remove NA, NaN, Inf values
@@ -174,15 +174,14 @@ custom_ROCR_risk <- function(measure, cutoff = 0.5, name = NULL, ...) {
   }
 }
 
-utils::globalVariables(c("id", "loss", "obs", "pred", "wts"))
 #' Cross-validated Risk Estimation
 #'
 #' Estimates the cross-validated risk for a given learner and evaluation
 #' function, which can be either a loss or a risk function.
 #'
 #' @param learner A trained learner object.
 #' @param eval_fun A valid loss or risk function. See
-#' \code{\link{loss_functions}} and \code{\link{risk_functions}}.
+#'  \code{\link{loss_functions}} and \code{\link{risk_functions}}.
 #' @param coefs A \code{numeric} vector of coefficients.
 #'
 #' @importFrom assertthat assert_that

R/zzz.R

@@ -38,8 +38,7 @@ sl3Options <- function(o, value) {
     }
     if (is.null(value)) {
       res[o] <- list(NULL)
-    }
-    else {
+    } else {
       res[[o]] <- value
     }
     options(res[o])