Lrnr_hal9001 weights

R/Lrnr_cv.R

@@ -76,22 +76,22 @@ interpret_fold_number <- function(fold_number) {
 #'   This can then be accessed with predict_fold(task, fold_number="full")
 #'   }
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(origami)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' covars <- c(
 #'   "apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn"
 #' )
 #' outcome <- "haz"
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
 #' glm_learner <- Lrnr_glm$new()
 #' cv_glm <- Lrnr_cv$new(glm_learner, folds = make_folds(cpp_imputed, V = 10))
-#' 
+#'
 #' # train cv learner
 #' cv_glm_fit <- cv_glm$train(task)
 #' preds <- cv_glm_fit$predict()

R/Lrnr_dbarts.R

@@ -99,18 +99,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' set.seed(123)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
 #' dbart_learner <- make_learner(Lrnr_dbarts, ndpost = 200)
-#' 
+#'
 #' # train dbart learner and make predictions
 #' dbart_fit <- dbart_learner$train(task)
 #' preds <- dbart_fit$predict()

R/Lrnr_density_discretize.R

@@ -23,17 +23,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train density discretize learner and make predictions
 #' lrnr_discretize <- Lrnr_density_discretize$new(
 #'   categorical_learner = Lrnr_glmnet$new()

R/Lrnr_density_hse.R

@@ -26,16 +26,17 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train density hse learner and make predictions
 #' lrnr_density_hse <- Lrnr_density_hse$new(mean_learner = Lrnr_glm$new())
 #' fit_density_hse <- lrnr_density_hse$train(task)

R/Lrnr_density_semiparametric.R

@@ -26,16 +26,17 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train density hse learner and make predictions
 #' lrnr_density_semi <- Lrnr_density_semiparametric$new(
 #'   mean_learner = Lrnr_glm$new()

R/Lrnr_grf.R

@@ -68,17 +68,18 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train grf learner and make predictions
 #' lrnr_grf <- Lrnr_grf$new(seed = 123)
 #' lrnr_grf_fit <- lrnr_grf$train(task)

R/Lrnr_gts.R

@@ -59,27 +59,27 @@
 #'     are going to be used.}
 #' }
 #'
-#' @examples 
+#' @examples
 #' # Example adapted from hts package manual
 #' # The hierarchical structure looks like 2 child nodes associated with level 1,
 #' # which are followed by 3 and 2 sub-child nodes respectively at level 2.
 #' library(hts)
-#' 
+#'
 #' set.seed(3274)
 #' abc <- as.data.table(5 + matrix(sort(rnorm(200)), ncol = 4, nrow = 50))
 #' setnames(abc, paste("Series", 1:ncol(abc), sep = "_"))
 #' abc[, time := .I]
 #' grps <- rbind(c(1, 1, 2, 2), c(1, 2, 1, 2))
 #' horizon <- 12
 #' suppressWarnings(abc_long <- melt(abc, id = "time", variable.name = "series"))
-#' 
+#'
 #' # create sl3 task (no outcome for hierarchical/grouped series)
 #' node_list <- list(outcome = "value", time = "time", id = "series")
 #' train_task <- sl3_Task$new(data = abc_long, nodes = node_list)
 #' test_data <- expand.grid(time = 51:55, series = unique(abc_long$series))
 #' test_data <- as.data.table(test_data)[, value := 0]
 #' test_task <- sl3_Task$new(data = test_data, nodes = node_list)
-#' 
+#'
 #' gts_learner <- Lrnr_gts$new()
 #' gts_learner_fit <- gts_learner$train(train_task)
 #' gts_learner_preds <- gts_learner_fit$predict(test_task)

R/Lrnr_h2o_glm.R

@@ -69,19 +69,20 @@ define_h2o_X <- function(task, outcome_type = NULL) {
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' library(h2o)
 #' suppressWarnings(h2o.init())
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(
-#'   cpp_imputed, 
-#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"), 
-#'   outcome = "haz")
-#' 
+#'   cpp_imputed,
+#'   covariates = c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs"),
+#'   outcome = "haz"
+#' )
+#'
 #' # train h2o glm learner and make predictions
 #' lrnr_h2o <- Lrnr_h2o_glm$new()
 #' lrnr_h2o_fit <- lrnr_h2o$train(task)

R/Lrnr_h2o_grid.R

@@ -43,12 +43,12 @@
 #' }
 #'
 #' @template common_parameters
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(h2o)
 #' suppressWarnings(h2o.init())
 #' set.seed(1)
-#' 
+#'
 #' # load example data
 #' data(cpp_imputed)
 #' covars <- c(
@@ -57,13 +57,15 @@
 #' )
 #' outcome <- "haz"
 #' cpp_imputed <- cpp_imputed[1:150, ]
-#' 
+#'
 #' # create sl3 task
 #' task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
-#' 
+#'
 #' # h2o grid search hyperparameter alpha
-#' h2o_glm_grid <- Lrnr_h2o_grid$new(algorithm = "glm",
-#'                                   hyper_params = list(alpha = c(0, 0.5)))
+#' h2o_glm_grid <- Lrnr_h2o_grid$new(
+#'   algorithm = "glm",
+#'   hyper_params = list(alpha = c(0, 0.5))
+#' )
 #' h2o_glm_grid_fit <- h2o_glm_grid$train(task)
 #' pred <- h2o_glm_grid_fit$predict()
 Lrnr_h2o_grid <- R6Class(

R/Lrnr_hal9001.R

@@ -92,7 +92,6 @@ Lrnr_hal9001 <- R6Class(
         verbose <- getOption("sl3.verbose")
       }
 
-
       args$X <- as.matrix(task$X)
 
       outcome_type <- self$get_outcome_type(task)
@@ -125,7 +124,11 @@ Lrnr_hal9001 <- R6Class(
       }
 
       if (task$has_node("weights")) {
-        args$weights <- task$weights
+        if (packageVersion("hal9001") >= "0.4.5") {
+          args$weights <- task$weights
+        } else {
+          args$fit_control$weights <- task$weights
+        }
       }
 
       if (task$has_node("offset")) {

R/Lrnr_independent_binomial.R

@@ -27,21 +27,22 @@
 #'
 #' @template common_parameters
 #'
-#' @examples 
+#' @examples
 #' library(dplyr)
-#' 
+#'
 #' # load example data
 #' data(cpp)
 #' cpp <- cpp %>%
 #'   select(c(bmi, agedays, feeding)) %>%
 #'   mutate(feeding = as.factor(feeding)) %>%
 #'   na.omit()
-#' 
+#'
 #' # create sl3 task
-#' task <- make_sl3_Task(cpp, 
-#'                       covariates = c("agedays", "bmi"), 
-#'                       outcome = "feeding")
-#' 
+#' task <- make_sl3_Task(cpp,
+#'   covariates = c("agedays", "bmi"),
+#'   outcome = "feeding"
+#' )
+#'
 #' # train independent binomial learner and make predictions
 #' lrnr_indbinomial <- make_learner(Lrnr_independent_binomial)
 #' fit <- lrnr_indbinomial$train(task)

R/Lrnr_multiple_ts.R

@@ -26,13 +26,13 @@
 #'    \code{learner$train}. See its documentation for details.
 #'   }
 #' }
-#' 
-#' @examples 
+#'
+#' @examples
 #' library(origami)
 #' library(dplyr)
-#' 
+#'
 #' set.seed(123)
-#' 
+#'
 #' # Simulate simple AR(2) process
 #' data <- matrix(arima.sim(model = list(ar = c(.9, -.2)), n = 200))
 #' id <- c(rep("Series_1", 50), rep("Series_2", 50), rep("Series_3", 50), rep("Series_4", 50))
@@ -41,36 +41,36 @@
 #' data <- data %>%
 #'   group_by(id) %>%
 #'   dplyr::mutate(time = 1:n())
-#' 
+#'
 #' data$W1 <- rbinom(200, 1, 0.6)
 #' data$W2 <- rbinom(200, 1, 0.2)
-#' 
+#'
 #' data <- as.data.table(data)
-#' 
+#'
 #' folds <- origami::make_folds(data,
-#'                              t = max(data$time),
-#'                              id = data$id,
-#'                              time = data$time,
-#'                              fold_fun = folds_rolling_window_pooled,
-#'                              window_size = 20,
-#'                              validation_size = 15,
-#'                              gap = 0,
-#'                              batch = 10
+#'   t = max(data$time),
+#'   id = data$id,
+#'   time = data$time,
+#'   fold_fun = folds_rolling_window_pooled,
+#'   window_size = 20,
+#'   validation_size = 15,
+#'   gap = 0,
+#'   batch = 10
 #' )
-#' 
+#'
 #' task <- sl3_Task$new(
 #'   data = data, outcome = "data",
 #'   time = "time", id = "id",
 #'   covariates = c("W1", "W2"),
 #'   folds = folds
 #' )
-#' 
+#'
 #' train_task <- training(task, fold = task$folds[[1]])
 #' valid_task <- validation(task, fold = task$folds[[1]])
-#' 
+#'
 #' lrnr_arima <- Lrnr_arima$new()
 #' multiple_ts_arima <- Lrnr_multiple_ts$new(learner = lrnr_arima)
-#' 
+#'
 #' multiple_ts_arima_fit <- multiple_ts_arima$train(train_task)
 #' multiple_ts_arima_preds <- multiple_ts_arima_fit$predict(valid_task)
 Lrnr_multiple_ts <- R6Class(