refactor: adjust date col reflection

R/ResamplingForecastCV.R

@@ -82,12 +82,13 @@ ResamplingForecastCV = R6Class("ResamplingForecastCV",
 
   private = list(
     .sample = function(ids, task, ...) {
-      if (length(task$col_roles$order) == 0L) {
-        stopf(
-          "Resampling '%s' requires an ordered task, but Task '%s' has no order.",
-          self$id, task$id
-        )
-      }
+      # if (length(task$col_roles$order) == 0L) {
+      #   stopf(
+      #     "Resampling '%s' requires an ordered task, but Task '%s' has no order.",
+      #     self$id, task$id
+      #   )
+      # }
+
       pars = self$param_set$get_values()
       ids = sort(ids)
       train_end = ids[ids <= (max(ids) - pars$horizon) & ids >= pars$window_size]

R/ResamplingForecastHoldout.R

@@ -68,12 +68,13 @@ ResamplingForecastHoldout = R6Class("ResamplingForecastHoldout",
 
   private = list(
     .sample = function(ids, task, ...) {
-      if (length(task$col_roles$order) == 0L) {
-        stopf(
-          "Resampling '%s' requires an ordered task, but Task '%s' has no order.",
-          self$id, task$id
-        )
-      }
+      # if (length(task$col_roles$order) == 0L) {
+      #   stopf(
+      #     "Resampling '%s' requires an ordered task, but Task '%s' has no order.",
+      #     self$id, task$id
+      #   )
+      # }
+
       pars = self$param_set$get_values()
       ratio = pars$ratio
       n = pars$n

R/TaskFcstAirpassengers.R

@@ -18,7 +18,7 @@ load_task_airpassengers = function(id = "airpassengers") {
   if (!requireNamespace("tsbox", quietly = TRUE)) {
     stopf("Package 'tsbox' is required to load the 'AirPassengers' dataset.")
   }
-  dt = tsbox::ts_dt(load_dataset("AirPassengers", package = "datasets"))
+  dt = tsbox::ts_dt(load_dataset("AirPassengers", "datasets"))
   setnames(dt, c("date", "passengers"))
   b = as_data_backend(dt)
 

R/zzz.R

@@ -10,7 +10,8 @@ mlr3forecast_resamplings = new.env()
 mlr3forecast_tasks = new.env()
 mlr3forecast_learners = new.env()
 mlr3forecast_measures = new.env()
-mlr3forecast_feature_types = c(date = "Date")
+# TODO: check if this can be moved to mlr3, copy components from @mllg PR
+mlr3forecast_feature_types = c(dte = "Date")
 
 named_union = function(x, y) set_names(union(x, y), union(names(x), names(y)))
 
@@ -35,7 +36,6 @@ register_mlr3 = function() {
     "fcst", "mlr3forecast", "TaskFcst", "LearnerFcst", "PredictionFcst", "PredictionDataFcst", "MeasureFcst" # nolint
   ), fill = TRUE), "type")
   mlr_reflections$learner_predict_types$fcst = mlr_reflections$learner_predict_types$regr
-  mlr_reflections$task_col_roles$fcst = union(mlr_reflections$task_col_roles$regr, "index")
   mlr_reflections$task_feature_types = named_union(
     mlr_reflections$task_feature_types, mlr3forecast_feature_types
   )
@@ -79,7 +79,7 @@ register_mlr3 = function() {
   mlr_reflections$task_types = mlr_reflections$task_types[!"fcst"]
   mlr_reflections$task_feature_types =
     mlr_reflections$task_feature_types[mlr_reflections$task_feature_types %nin% mlr3forecast_feature_types] # nolint
-  reflections = c("learner_predict_types", "task_col_roles", "task_properties")
+  reflections = c("learner_predict_types", "task_properties")
   walk(reflections, function(x) mlr_reflections[[x]] = remove_named(mlr_reflections[[x]], "fcst"))
 }
 

README.md

@@ -45,32 +45,32 @@ prediction = ff$predict_newdata(newdata, task)
 prediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1    NA 452.7575
-#>        2    NA 474.8485
-#>        3    NA 481.4720
+#>        1    NA 446.9409
+#>        2    NA 477.9439
+#>        3    NA 480.5694
 prediction = ff$predict(task, 142:144)
 prediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   461 458.1226
-#>        2   390 412.7669
-#>        3   432 396.2460
+#>        1   461 459.4145
+#>        2   390 411.2457
+#>        3   432 400.4514
 prediction$score(measure)
 #> regr.rmse 
-#>  24.52863
+#>  21.97883
 
 ff = Forecaster$new(lrn("regr.ranger"), 1:3)
 resampling = rsmp("forecast_holdout", ratio = 0.8)
 rr = resample(task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  112.7031
+#>  105.0997
 
 resampling = rsmp("forecast_cv")
 rr = resample(task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  48.80621
+#>  54.93903
 ```
 
 ### Multivariate
@@ -90,34 +90,34 @@ ff = Forecaster$new(lrn("regr.ranger"), 1:3)$train(new_task)
 prediction = ff$predict(new_task, 142:144)
 prediction$score(measure)
 #> regr.rmse 
-#>  19.26131
+#>  17.55705
 
 row_ids = new_task$nrow - 0:2
 ff$predict_newdata(new_task$data(rows = row_ids), new_task)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   432 407.1069
-#>        2   390 391.4622
-#>        3   461 393.6115
+#>        1   432 405.2216
+#>        2   390 388.3066
+#>        3   461 385.6412
 newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
 ff$predict_newdata(newdata, new_task)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1    NA 407.1069
-#>        2    NA 391.4622
-#>        3    NA 393.6115
+#>        1    NA 405.2216
+#>        2    NA 388.3066
+#>        3    NA 385.6412
 
 resampling = rsmp("forecast_holdout", ratio = 0.8)
 rr = resample(new_task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  81.82989
+#>  82.35283
 
 resampling = rsmp("forecast_cv")
 rr = resample(new_task, ff, resampling)
 rr$aggregate(measure)
 #> regr.rmse 
-#>  45.80208
+#>  45.54337
 ```
 
 ### mlr3pipelines integration
@@ -128,7 +128,7 @@ glrn = as_learner(graph %>>% ff)$train(task)
 prediction = glrn$predict(task, 142:144)
 prediction$score(measure)
 #> regr.rmse 
-#>  34.39579
+#>  34.29322
 ```
 
 ### Example: Forecasting electricity demand
@@ -166,11 +166,11 @@ prediction = glrn$predict_newdata(newdata, task)
 prediction
 #> <PredictionRegr> for 14 observations:
 #>  row_ids truth response
-#>        1    NA 187.1619
-#>        2    NA 191.8612
-#>        3    NA 184.2280
+#>        1    NA 187.9399
+#>        2    NA 190.5695
+#>        3    NA 184.2617
 #>      ---   ---      ---
-#>       12    NA 214.1141
-#>       13    NA 216.5287
-#>       14    NA 217.9717
+#>       12    NA 214.6350
+#>       13    NA 218.8392
+#>       14    NA 221.4170
 ```