less computationally expensive examples

R/plot_contribution.R

@@ -26,17 +26,16 @@
 #'
 #'
 #' @examples
-#' \dontrun{
 #' library(xgboost)
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' target <- fifa20$target
 #' param <- list(objective = "reg:squarederror", max_depth = 3)
-#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200)
+#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target,
+#'                               nrounds = 20, verbose = FALSE)
 #' unified_model <- xgboost.unify(xgb_model, as.matrix(data))
 #' x <- head(data, 1)
 #' shap <- treeshap(unified_model, x)
 #' plot_contribution(shap, 1,  min_max = c(0, 120000000))
-#' }
 plot_contribution <- function(treeshap,
                               obs = 1,
                               max_vars = 5,

R/plot_feature_dependence.R

@@ -20,17 +20,16 @@
 #'
 #'
 #' @examples
-#' \dontrun{
 #' library(xgboost)
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' target <- fifa20$target
 #' param <- list(objective = "reg:squarederror", max_depth = 3)
-#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200)
+#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target,
+#'                               nrounds = 20, verbose = FALSE)
 #' unified_model <- xgboost.unify(xgb_model, as.matrix(data))
 #' x <- head(data, 100)
 #' shaps <- treeshap(unified_model, x)
 #' plot_feature_dependence(shaps, variable = "overall")
-#' }
 plot_feature_dependence <- function(treeshap, variable,
                                     title = "Feature Dependence", subtitle = NULL) {
 

R/plot_feature_importance.R

@@ -24,16 +24,15 @@
 #'
 #'
 #' @examples
-#' \dontrun{
 #' library(xgboost)
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' target <- fifa20$target
 #' param <- list(objective = "reg:squarederror", max_depth = 3)
-#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target, nrounds = 200)
+#' xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target,
+#'                               nrounds = 20, verbose = FALSE)
 #' unified_model <- xgboost.unify(xgb_model, as.matrix(data))
 #' shaps <- treeshap(unified_model, as.matrix(head(data, 3)))
 #' plot_feature_importance(shaps, max_vars = 4)
-#' }
 plot_feature_importance <- function(treeshap,
                                     desc_sorting = TRUE,
                                     max_vars = ncol(shaps),

R/plot_interaction.R

@@ -22,15 +22,13 @@
 #'
 #'
 #' @examples
-#' \donttest{
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' target <- fifa20$target
 #' param2 <- list(objective = "reg:squarederror", max_depth = 5)
 #' xgb_model2 <- xgboost::xgboost(as.matrix(data), params = param2, label = target, nrounds = 10)
 #' unified_model2 <- xgboost.unify(xgb_model2, data)
 #' inters <- treeshap(unified_model2, as.matrix(data[1:50, ]), interactions = TRUE)
 #' plot_interaction(inters, "dribbling", "defending")
-#' }
 plot_interaction <- function(treeshap, var1, var2,
                              title = "SHAP Interaction Value Plot",
                              subtitle = "") {

R/predict.R

@@ -11,21 +11,18 @@
 #' @export
 #'
 #' @examples
-#' \dontrun{
 #' library(gbm)
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' data['value_eur'] <- fifa20$target
 #' gbm_model <- gbm::gbm(
-#'              formula = value_eur ~ .,
-#'              data = data,
-#'              distribution = "laplace",
-#'              n.trees = 1000,
-#'              cv.folds = 2,
-#'              interaction.depth = 2,
-#'              n.cores = 1)
-#' unified <- gbm.unify(gbm_model, data)
-#' predict(unified, data[3:7, ])
-#'}
+#'   formula = value_eur ~ .,
+#'   data = data,
+#'   distribution = "laplace",
+#'   n.trees = 20,
+#'   interaction.depth = 4,
+#'   n.cores = 1)
+#'   unified <- gbm.unify(gbm_model, data)
+#'   predict(unified, data[2001:2005, ])
 predict.model_unified <- function(object, x, ...) {
   unified_model <- object
   model <- unified_model$model

R/set_reference_dataset.R

@@ -26,21 +26,18 @@
 #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}}
 #'
 #' @examples
-#' \dontrun{
 #' library(gbm)
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' data['value_eur'] <- fifa20$target
 #' gbm_model <- gbm::gbm(
-#'              formula = value_eur ~ .,
-#'              data = data,
-#'              distribution = "laplace",
-#'              n.trees = 1000,
-#'              cv.folds = 2,
-#'              interaction.depth = 2,
-#'              n.cores = 1)
+#' formula = value_eur ~ .,
+#'   data = data,
+#'   distribution = "laplace",
+#'   n.trees = 20,
+#'   interaction.depth = 4,
+#'   n.cores = 1)
 #' unified <- gbm.unify(gbm_model, data)
 #' set_reference_dataset(unified, data[200:700, ])
-#'}
 set_reference_dataset <- function(unified_model, x) {
   model <- unified_model$model
   data <- x

R/unify_gbm.R

@@ -21,21 +21,19 @@
 #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}}
 #'
 #' @examples
-#'\donttest{
 #' library(gbm)
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' data['value_eur'] <- fifa20$target
 #' gbm_model <- gbm::gbm(
 #'              formula = value_eur ~ .,
 #'              data = data,
 #'              distribution = "gaussian",
-#'              n.trees = 50,
+#'              n.trees = 20,
 #'              interaction.depth = 4,
 #'              n.cores = 1)
 #' unified_model <- gbm.unify(gbm_model, data)
 #' shaps <- treeshap(unified_model, data[1:2,])
 #' plot_contribution(shaps, obs = 1)
-#' }
 gbm.unify <- function(gbm_model, data) {
   if(!inherits(gbm_model,'gbm')) {
     stop('Object gbm_model was not of class "gbm"')

R/unify_lightgbm.R

@@ -25,9 +25,9 @@
 #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}}
 #'
 #' @examples
-#' \donttest{
 #' library(lightgbm)
-#' param_lgbm <- list(objective = "regression", max_depth = 2,  force_row_wise = TRUE)
+#' param_lgbm <- list(objective = "regression", max_depth = 2,
+#'                    force_row_wise = TRUE, num_iterations = 20)
 #' data_fifa <- fifa20$data[!colnames(fifa20$data) %in%
 #'              c('work_rate', 'value_eur', 'gk_diving', 'gk_handling',
 #'              'gk_kicking', 'gk_reflexes', 'gk_speed', 'gk_positioning')]
@@ -40,7 +40,6 @@
 #' unified_model <- lightgbm.unify(lgb_model, sparse_data)
 #' shaps <- treeshap(unified_model, data[1:2, ])
 #' plot_contribution(shaps, obs = 1)
-#' }
 lightgbm.unify <- function(lgb_model, data, recalculate = FALSE) {
   if (!requireNamespace("lightgbm", quietly = TRUE)) {
     stop("Package \"lightgbm\" needed for this function to work. Please install it.",

R/unify_xgboost.R

@@ -21,7 +21,6 @@
 #' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}}
 #'
 #' @examples
-#' \donttest{
 #' library(xgboost)
 #' data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
 #' target <- fifa20$target
@@ -31,7 +30,6 @@
 #' unified_model <- xgboost.unify(xgb_model, as.matrix(data))
 #' shaps <- treeshap(unified_model, data[1:2,])
 #' plot_contribution(shaps, obs = 1)
-#' }
 #'
 xgboost.unify <- function(xgb_model, data, recalculate = FALSE) {
   if (!requireNamespace("xgboost", quietly = TRUE)) {