feat: more params for {forecast} learners

R/LearnerRegrArfima.R

@@ -20,7 +20,25 @@ LearnerFcstArfima = R6Class("LearnerFcstArfima",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      param_set = ps()
+      param_set = ps(
+        drange = p_uty(default = c(0, 0.5), tags = "train"),
+        estim = p_fct(default = "mle", levels = c("mle", "ls"), tags = "train"),
+        lambda = p_uty(default = NULL, tags = "train"),
+        order = p_uty(
+          default = c(0L, 0L, 0L),
+          tags = "train",
+          custom_check = crate(function(x) check_integerish(x, lower = 0L, len = 3L))
+        ),
+        seasonal = p_uty(
+          default = c(0L, 0L, 0L),
+          tags = "train",
+          custom_check = crate(function(x) check_integerish(x, lower = 0L, len = 3L))
+        ),
+        include.mean = p_lgl(default = TRUE, tags = "train"),
+        include.drift = p_lgl(default = FALSE, tags = "train"),
+        biasadj = p_lgl(default = FALSE, tags = "train"),
+        method = p_fct(c("CSS-ML", "ML", "CSS"), default = "CSS-ML", tags = "train")
+      )
 
       super$initialize(
         id = "fcst.arfima",
@@ -46,9 +64,8 @@ LearnerFcstArfima = R6Class("LearnerFcstArfima",
         pv = insert_named(pv, list(weights = task$weights$weight))
       }
 
+      xreg = NULL
       if (is_task_featureless(task)) {
-        xreg = NULL
-      } else {
         xreg = as.matrix(task$data(cols = fcst_feature_names(task)))
       }
       invoke(forecast::arfima,

R/LearnerRegrArima.R

@@ -58,9 +58,8 @@ LearnerFcstArima = R6Class("LearnerFcstArima",
         pv = insert_named(pv, list(weights = task$weights$weight))
       }
 
+      xreg = NULL
       if (is_task_featureless(task)) {
-        xreg = NULL
-      } else {
         xreg = as.matrix(task$data(cols = fcst_feature_names(task)))
       }
       invoke(forecast::Arima,

R/LearnerRegrAutoArima.R

@@ -63,9 +63,8 @@ LearnerFcstAutoArima = R6Class("LearnerFcstAutoArima",
         pv = insert_named(pv, list(weights = task$weights$weight))
       }
 
+      xreg = NULL
       if (is_task_featureless(task)) {
-        xreg = NULL
-      } else {
         xreg = as.matrix(task$data(cols = fcst_feature_names(task)))
       }
       invoke(forecast::auto.arima,

R/LearnerRegrEts.R

@@ -21,7 +21,11 @@ LearnerFcstEts = R6Class("LearnerFcstEts",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        model = p_uty(default = "ZZZ", tags = "train", custom_check = crate(function(x) check_string(x, n.chars = 3L))),
+        model = p_uty(
+          default = "ZZZ",
+          tags = "train",
+          custom_check = crate(function(x) check_string(x, n.chars = 3L))
+        ),
         damped = p_lgl(default = NULL, special_vals = list(NULL), tags = "train"),
         alpha = p_dbl(default = NULL, special_vals = list(NULL), tags = "train"),
         beta = p_dbl(default = NULL, special_vals = list(NULL), tags = "train"),
@@ -32,13 +36,22 @@ LearnerFcstEts = R6Class("LearnerFcstEts",
         biasadj = p_lgl(default = FALSE, tags = "train"),
         lower = p_uty(default = c(rep(1e-04, 3), 0.8), tags = "train"),
         upper = p_uty(default = c(rep(0.9999, 3), 0.98), tags = "train"),
-        opt.crit = p_fct(default = "lik", levels = c("lik", "amse", "mse", "sigma", "mae"), tags = "train"),
+        opt.crit = p_fct(
+          default = "lik",
+          levels = c("lik", "amse", "mse", "sigma", "mae"),
+          tags = "train"
+        ),
         nmse = p_int(0L, 30L, default = 3, tags = "train"),
-        bounds = p_fct(default = "both", levels = c("both", "usual", "admissible"), tags = "train"),
+        bounds = p_fct(
+          default = "both", levels = c("both", "usual", "admissible"), tags = "train"
+        ),
         ic = p_fct(default = "aicc", levels = c("aicc", "aic", "bic"), tags = "train"),
         restrict = p_lgl(default = TRUE, tags = "train"),
         allow.multiplicative.trend = p_lgl(default = FALSE, tags = "train"),
-        na.action = p_fct(default = "na.contiguous", levels = c("na.contiguous", "na.interp", "na.fail"))
+        na.action = p_fct(
+          default = "na.contiguous",
+          levels = c("na.contiguous", "na.interp", "na.fail")
+        )
       )
 
       super$initialize(

R/LearnerRegrForecast.R

@@ -32,7 +32,6 @@ LearnerRegrForecast = R6Class("LearnerRegrForecast",
         return(list(response = as.numeric(pred$mean)))
       }
 
-      # might not be robust enough with position instead of name
       pred$lower = pred$lower[, rev(seq_len(ncol(pred$lower)))]
       quantiles = cbind(
         pred$lower,