diff --git a/index.html b/index.html index 4cafff4..29fb6eb 100644 --- a/index.html +++ b/index.html @@ -82,11 +82,122 @@

InstallationUsage

-

Univariate +

Example: native forecast learner

+

First lets create a helper function to generate new data for forecasting tasks.

 library(mlr3forecast)
-library(mlr3learners)
+#> Loading required package: mlr3
+
+generate_newdata = function(task, n = 1L, resolution = "day") {
+  assert_count(n)
+  assert_string(resolution)
+  assert_choice(
+    resolution, c("second", "minute", "hour", "day", "week", "month", "quarter", "year")
+  )
+
+  order_cols = task$col_roles$order
+  target = task$target_names
+  max_index = max(task$data(cols = order_cols)[[1L]])
+
+  unit = switch(resolution,
+    second = "sec",
+    minute = "min",
+    hour = ,
+    day = ,
+    week = ,
+    month = ,
+    quarter = ,
+    year = identity(resolution),
+    stopf("Invalid resolution")
+  )
+  unit = sprintf("1 %s", unit)
+  index = seq(max_index, length.out = n + 1L, by = unit)
+  index = index[2:length(index)]
+
+  newdata = data.table(index = index, target = rep(NA_real_, n))
+  setnames(newdata, c(order_cols, target))
+  setDF(newdata)[]
+}
+
+task = tsk("airpassengers")
+newdata = generate_newdata(task, 12L, "month")
+newdata
+#>          date passengers
+#> 1  1961-01-01         NA
+#> 2  1961-02-01         NA
+#> 3  1961-03-01         NA
+#> 4  1961-04-01         NA
+#> 5  1961-05-01         NA
+#> 6  1961-06-01         NA
+#> 7  1961-07-01         NA
+#> 8  1961-08-01         NA
+#> 9  1961-09-01         NA
+#> 10 1961-10-01         NA
+#> 11 1961-11-01         NA
+#> 12 1961-12-01         NA
+

Currently, we support native forecasting learners from the forecast package. In the future, we plan to support more forecasting learners.

+
+task = tsk("airpassengers")
+learner = lrn("fcst.arima", order = c(2L, 1L, 2L))$train(task)
+#> Registered S3 method overwritten by 'quantmod':
+#>   method            from
+#>   as.zoo.data.frame zoo
+prediction = learner$predict(task, 140:144)
+prediction$score(msr("regr.rmse"))
+#> regr.rmse 
+#>  50.62826
+newdata = generate_newdata(task, 12L, "month")
+learner$predict_newdata(newdata, task)
+#> <PredictionRegr> for 12 observations:
+#>  row_ids truth response
+#>        1    NA 483.8637
+#>        2    NA 465.9727
+#>        3    NA 469.4676
+#>      ---   ---      ---
+#>       10    NA 466.3308
+#>       11    NA 466.2953
+#>       12    NA 466.2723
+
+learner = lrn("fcst.auto_arima")$train(task)
+prediction = learner$predict(task, 140:144)
+prediction$score(msr("regr.rmse"))
+#> regr.rmse 
+#>  39.62379
+newdata = generate_newdata(task, 12L, "month")
+learner$predict_newdata(newdata, task)
+#> <PredictionRegr> for 12 observations:
+#>  row_ids truth response
+#>        1    NA 483.3799
+#>        2    NA 490.9993
+#>        3    NA 520.2773
+#>      ---   ---      ---
+#>       10    NA 500.2729
+#>       11    NA 507.3034
+#>       12    NA 512.9829
+
+# works with quantile response
+learner = lrn("fcst.auto_arima",
+  predict_type = "quantiles",
+  quantiles = c(0.1, 0.15, 0.5, 0.85, 0.9),
+  quantile_response = 0.5
+)$train(task)
+learner$predict_newdata(newdata, task)
+#> <PredictionRegr> for 12 observations:
+#>  row_ids truth     q0.1    q0.15     q0.5    q0.85     q0.9 response
+#>        1    NA 449.3201 455.8346 483.3799 510.9252 517.4397 483.3799
+#>        2    NA 439.6752 449.4918 490.9993 532.5069 542.3235 490.9993
+#>        3    NA 464.0693 474.8200 520.2773 565.7347 576.4854 520.2773
+#>      ---   ---      ---      ---      ---      ---      ---      ---
+#>       10    NA 440.1583 451.6562 500.2729 548.8896 560.3875 500.2729
+#>       11    NA 446.7823 458.3580 507.3034 556.2489 567.8246 507.3034
+#>       12    NA 452.1168 463.7584 512.9829 562.2074 573.8491 512.9829
+
+
+

machine learning forecasting +

+
+library(mlr3learners)
 
 task = tsk("airpassengers")
 task$select(setdiff(task$feature_names, "date"))
@@ -96,39 +207,38 @@ 
Univariateprediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1    NA 435.7156
-#>        2    NA 435.9651
-#>        3    NA 455.7526
+#>        1    NA 434.0749
+#>        2    NA 436.2586
+#>        3    NA 456.6124
 prediction = flrn$predict(task, 142:144)
 prediction
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   461 456.1817
-#>        2   390 412.6326
-#>        3   432 429.3494
+#>        1   461 456.0759
+#>        2   390 410.5727
+#>        3   432 430.4355
 prediction$score(msr("regr.rmse"))
 #> regr.rmse 
-#>  13.44712
+#>   12.2465
 
 flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)
 resampling = rsmp("forecast_holdout", ratio = 0.9)
 rr = resample(task, flrn, resampling)
 rr$aggregate(msr("regr.rmse"))
 #> regr.rmse 
-#>  48.43771
+#>  46.06935
 
 resampling = rsmp("forecast_cv")
 rr = resample(task, flrn, resampling)
 rr$aggregate(msr("regr.rmse"))
 #> regr.rmse 
-#>  26.80632
+#> 27.64328

Multivariate

-
-library(mlr3learners)
-library(mlr3pipelines)
+
+library(mlr3pipelines)
 
 task = tsk("airpassengers")
 # datefeatures currently requires POSIXct
@@ -141,39 +251,39 @@ 
Multivariateprediction = flrn$predict(new_task, 142:144)
 prediction$score(msr("regr.rmse"))
 #> regr.rmse 
-#>  13.92394
+#>  15.12504
 
 row_ids = new_task$nrow - 0:2
 flrn$predict_newdata(new_task$data(rows = row_ids), new_task)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1   432 432.6353
-#>        2   390 434.4334
-#>        3   461 456.4304
+#>        1   432 434.7273
+#>        2   390 434.9359
+#>        3   461 454.7645
 newdata = new_task$data(rows = row_ids, cols = new_task$feature_names)
 flrn$predict_newdata(newdata, new_task)
 #> <PredictionRegr> for 3 observations:
 #>  row_ids truth response
-#>        1    NA 432.6353
-#>        2    NA 434.4334
-#>        3    NA 456.4304
+#>        1    NA 434.7273
+#>        2    NA 434.9359
+#>        3    NA 454.7645
 
 resampling = rsmp("forecast_holdout", ratio = 0.9)
 rr = resample(new_task, flrn, resampling)
 rr$aggregate(msr("regr.rmse"))
 #> regr.rmse 
-#>  47.94459
+#>  49.02976
 
 resampling = rsmp("forecast_cv")
 rr = resample(new_task, flrn, resampling)
 rr$aggregate(msr("regr.rmse"))
 #> regr.rmse 
-#>  26.71381

+#>  24.71841

mlr3pipelines integration

-
+
 graph = ppl("convert_types", "Date", "POSIXct") %>>%
   po("datefeatures",
     param_vals = list(is_day = FALSE, hour = FALSE, minute = FALSE, second = FALSE)
@@ -183,12 +293,12 @@ 
mlr3pipelines integrationprediction = glrn$predict(task, 142:144)
 prediction$score(msr("regr.rmse"))
 #> regr.rmse 
-#>  13.68004

+#>  13.82437

Example: Forecasting electricity demand

-
+
 library(mlr3learners)
 library(mlr3pipelines)
 
@@ -204,7 +314,7 @@ 
Example: Forecasting electricity
     ),
     by = date
   ] |>
-  as_task_fcst(target = "demand", index = "date")
+  as_task_fcst(id = "vic_elec", target = "demand", order = "date")
 
 graph = ppl("convert_types", "Date", "POSIXct") %>>%
   po("datefeatures",
@@ -226,18 +336,18 @@ 
Example: Forecasting electricity
 prediction
 #> <PredictionRegr> for 14 observations:
 #>  row_ids truth response
-#>        1    NA 187.4035
-#>        2    NA 191.8731
-#>        3    NA 183.8377
+#>        1    NA 187.4741
+#>        2    NA 192.1220
+#>        3    NA 184.4865
 #>      ---   ---      ---
-#>       12    NA 215.6015
-#>       13    NA 220.0975
-#>       14    NA 220.1057

+#>       12    NA 213.7619
+#>       13    NA 218.1695
+#>       14    NA 219.3505

Global Forecasting

-
+
 library(mlr3learners)
 library(mlr3pipelines)
 library(tsibble) # needs not be loaded for it to somehow work
@@ -248,7 +358,7 @@ 
Global Forecasting_[, month := as.Date(month)] |>
   _[, .(count = sum(count)), by = .(state, month)] |>
   setorder(state, month) |>
-  as_task_fcst(target = "count", index = "month", key = "state")
+  as_task_fcst(id = "aus_livestock", target = "count", order = "month", key = "state")
 
 graph = ppl("convert_types", "Date", "POSIXct") %>>%
   po("datefeatures",
@@ -265,19 +375,20 @@ 
Global Forecastingprediction = flrn$predict(task, 4460:4464)
 prediction$score(msr("regr.rmse"))
 #> regr.rmse 
-#>   21464.2
+#>  22703.54
 
 flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
 resampling = rsmp("forecast_holdout", ratio = 0.9)
 rr = resample(task, flrn, resampling)
 rr$aggregate(msr("regr.rmse"))
 #> regr.rmse 
-#>  90619.28

+#>  94349.09

Example: Global vs Local Forecasting

-
+
In machine learning forecasting the difference between forecasting a time series and longitudinal data is often refered to local and global forecasting.

+
 # TODO: find better task example, since the effect is minor here
 
 graph = ppl("convert_types", "Date", "POSIXct") %>>%
@@ -296,7 +407,7 @@ 
Example: Global vs Local Forecastin
   _[, month := as.Date(month)] |>
   _[state == "Western Australia", .(count = sum(count)), by = .(month)] |>
   setorder(month) |>
-  as_task_fcst(target = "count", index = "month")
+  as_task_fcst(id = "aus_livestock", target = "count", order = "month")
 task = graph$train(task)[[1L]]
 flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
 tab = task$backend$data(
@@ -307,7 +418,7 @@ 
Example: Global vs Local Forecastin
 prediction = flrn$predict(task, row_ids)
 prediction$score(msr("regr.rmse"))
 #> regr.rmse 
-#>  32641.16
+#>  32282.74
 
 # global forecasting
 task = tsibbledata::aus_livestock |>
@@ -316,7 +427,7 @@ 
Example: Global vs Local Forecastin
   _[, month := as.Date(month)] |>
   _[, .(count = sum(count)), by = .(state, month)] |>
   setorder(state, month) |>
-  as_task_fcst(target = "count", index = "month", key = "state")
+  as_task_fcst(id = "aus_livestock", target = "count", order = "month", key = "state")
 task = graph$train(task)[[1L]]
 task$col_roles$key = "state"
 flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
@@ -328,192 +439,7 @@ 
Example: Global vs Local Forecastin
 prediction = flrn$predict(task, row_ids)
 prediction$score(msr("regr.rmse"))
 #> regr.rmse 
-#>  32908.08

-
-
-

Example: generate new data -

-
-library(checkmate)
-
-generate_newdata = function(task, n = 1L, resolution = "day") {
-  assert_count(n)
-  assert_string(resolution)
-  assert_choice(
-    resolution, c("second", "minute", "hour", "day", "week", "month", "quarter", "year")
-  )
-
-  order_cols = task$col_roles$order
-  target = task$target_names
-  max_index = max(task$data(cols = order_cols)[[1L]])
-
-  unit = switch(resolution,
-    second = "sec",
-    minute = "min",
-    hour = ,
-    day = ,
-    week = ,
-    month = ,
-    quarter = ,
-    year = identity(resolution),
-    stopf("Invalid resolution")
-  )
-  unit = sprintf("1 %s", unit)
-  index = seq(max_index, length.out = n + 1L, by = unit)
-  index = index[2:length(index)]
-
-  newdata = data.table(index = index, target = rep(NA_real_, n))
-  setnames(newdata, c(order_cols, target))
-  setDF(newdata)[]
-}
-
-task = tsk("airpassengers")
-newdata = generate_newdata(task, 12L, "month")
-newdata
-#>          date passengers
-#> 1  1961-01-01         NA
-#> 2  1961-02-01         NA
-#> 3  1961-03-01         NA
-#> 4  1961-04-01         NA
-#> 5  1961-05-01         NA
-#> 6  1961-06-01         NA
-#> 7  1961-07-01         NA
-#> 8  1961-08-01         NA
-#> 9  1961-09-01         NA
-#> 10 1961-10-01         NA
-#> 11 1961-11-01         NA
-#> 12 1961-12-01         NA
-
-
-

Example: Native Forecasting Learners -

-
-task = tsk("airpassengers")
-learner = lrn("fcst.arima", order = c(2L, 1L, 2L))$train(task)
-#> Registered S3 method overwritten by 'quantmod':
-#>   method            from
-#>   as.zoo.data.frame zoo
-prediction = learner$predict(task, 140:144)
-prediction$score(msr("regr.rmse"))
-#> regr.rmse 
-#>  50.62826
-newdata = generate_newdata(task, 12L, "month")
-learner$predict_newdata(newdata, task)
-#> <PredictionRegr> for 12 observations:
-#>  row_ids truth response
-#>        1    NA 483.8637
-#>        2    NA 465.9727
-#>        3    NA 469.4676
-#>      ---   ---      ---
-#>       10    NA 466.3308
-#>       11    NA 466.2953
-#>       12    NA 466.2723
-
-learner = lrn("fcst.auto_arima")$train(task)
-prediction = learner$predict(task, 140:144)
-prediction$score(msr("regr.rmse"))
-#> regr.rmse 
-#>  39.62379
-newdata = generate_newdata(task, 12L, "month")
-learner$predict_newdata(newdata, task)
-#> <PredictionRegr> for 12 observations:
-#>  row_ids truth response
-#>        1    NA 483.3799
-#>        2    NA 490.9993
-#>        3    NA 520.2773
-#>      ---   ---      ---
-#>       10    NA 500.2729
-#>       11    NA 507.3034
-#>       12    NA 512.9829
-
-# works with quantile response
-learner = lrn("fcst.auto_arima",
-  predict_type = "quantiles",
-  quantiles = c(0.1, 0.15, 0.5, 0.85, 0.9),
-  quantile_response = 0.5
-)$train(task)
-learner$predict_newdata(newdata, task)
-#> <PredictionRegr> for 12 observations:
-#>  row_ids truth     q0.1    q0.15     q0.5    q0.85     q0.9 response
-#>        1    NA 449.3201 455.8346 483.3799 510.9252 517.4397 483.3799
-#>        2    NA 439.6752 449.4918 490.9993 532.5069 542.3235 490.9993
-#>        3    NA 464.0693 474.8200 520.2773 565.7347 576.4854 520.2773
-#>      ---   ---      ---      ---      ---      ---      ---      ---
-#>       10    NA 440.1583 451.6562 500.2729 548.8896 560.3875 500.2729
-#>       11    NA 446.7823 458.3580 507.3034 556.2489 567.8246 507.3034
-#>       12    NA 452.1168 463.7584 512.9829 562.2074 573.8491 512.9829
-
-task = tsk("airpassengers")
-learner = lrn("fcst.arfima")$train(task)
-prediction = learner$predict(task, 140:144)
-prediction$score(msr("regr.rmse"))
-#> regr.rmse 
-#>  54.93583
-newdata = generate_newdata(task, 12L, "month")
-learner$predict_newdata(newdata, task)
-#> <PredictionRegr> for 12 observations:
-#>  row_ids truth response
-#>        1    NA 470.3903
-#>        2    NA 449.1027
-#>        3    NA 452.4956
-#>      ---   ---      ---
-#>       10    NA 408.8267
-#>       11    NA 405.3927
-#>       12    NA 402.0429
-
-task = tsk("airpassengers")
-learner = lrn("fcst.ets")$train(task)
-prediction = learner$predict(task, 140:144)
-prediction$score(msr("regr.rmse"))
-#> regr.rmse 
-#>  61.44108
-newdata = generate_newdata(task, 12L, "month")
-learner$predict_newdata(newdata, task)
-#> <PredictionRegr> for 12 observations:
-#>  row_ids truth response
-#>        1    NA 431.9958
-#>        2    NA 431.9958
-#>        3    NA 431.9958
-#>      ---   ---      ---
-#>       10    NA 431.9958
-#>       11    NA 431.9958
-#>       12    NA 431.9958
-
-task = tsk("airpassengers")
-learner = lrn("fcst.tbats")$train(task)
-prediction = learner$predict(task, 140:144)
-prediction$score(msr("regr.rmse"))
-#> regr.rmse 
-#>  40.89975
-newdata = generate_newdata(task, 12L, "month")
-learner$predict_newdata(newdata, task)
-#> <PredictionRegr> for 12 observations:
-#>  row_ids truth response
-#>        1    NA 502.2486
-#>        2    NA 545.0701
-#>        3    NA 610.7134
-#>      ---   ---      ---
-#>       10    NA 592.3269
-#>       11    NA 613.4432
-#>       12    NA 633.9967
-
-task = tsk("airpassengers")
-learner = lrn("fcst.bats")$train(task)
-prediction = learner$predict(task, 140:144)
-prediction$score(msr("regr.rmse"))
-#> regr.rmse 
-#>  40.89975
-newdata = generate_newdata(task, 12L, "month")
-learner$predict_newdata(newdata, task)
-#> <PredictionRegr> for 12 observations:
-#>  row_ids truth response
-#>        1    NA 502.2486
-#>        2    NA 545.0701
-#>        3    NA 610.7134
-#>      ---   ---      ---
-#>       10    NA 592.3269
-#>       11    NA 613.4432
-#>       12    NA 633.9967
+#> 32095.1

Example: Custom PipeOps @@ -590,19 +516,19 @@

Example: Custom PipeOpsprediction = glrn$predict(task, 142:144) prediction$score(msr("regr.rmse")) #> regr.rmse -#> 24.22745 +#> 26.41252 newdata = generate_newdata(task, 12L, "month") glrn$predict_newdata(newdata, task) #> <PredictionRegr> for 12 observations: #> row_ids truth response -#> 1 NA 435.7197 -#> 2 NA 441.3132 -#> 3 NA 457.2861 +#> 1 NA 436.5384 +#> 2 NA 439.9788 +#> 3 NA 458.0152 #> --- --- --- -#> 10 NA 469.7691 -#> 11 NA 442.5062 -#> 12 NA 443.2276

+#> 10 NA 473.4481 +#> 11 NA 443.1554 +#> 12 NA 443.2785

Example: common target transformations @@ -641,7 +567,26 @@

Example: common target transforma prediction = glrn$predict(task, 142:144) prediction$score(msr("regr.rmse")) #> regr.rmse -#> 35.94069

+#> 35.41751 +
+graph = po("fcst.lag", lag = 1:12) %>>%
+  ppl("convert_types", "Date", "POSIXct") %>>%
+  po("datefeatures",
+    param_vals = list(
+      week_of_year = FALSE, day_of_week = FALSE, day_of_month = FALSE,
+      day_of_year = FALSE, is_day = FALSE, hour = FALSE, minute = FALSE,
+      second = FALSE
+    )
+  )
+
+task = tsk("airpassengers")
+flrn = ForecastRecursiveLearner$new(lrn("regr.ranger"))
+glrn = as_learner(graph %>>% flrn)
+trafo = po("fcst.targetdiff", lag = 12L)
+pipeline = ppl("targettrafo", graph = glrn, trafo_pipeop = trafo)
+glrn = as_learner(pipeline)$train(task)
+prediction = glrn$predict(task, 142:144)
+prediction$score(msr("regr.rmse"))
diff --git a/pkgdown.yml b/pkgdown.yml index 6de902e..b76a584 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.1 pkgdown_sha: ~ articles: {} -last_built: 2025-01-11T23:08Z +last_built: 2025-01-14T09:55Z urls: reference: https://mlr3forecast.mlr-org.com/reference article: https://mlr3forecast.mlr-org.com/articles diff --git a/reference/LearnerFcst.html b/reference/LearnerFcst.html index 1b0bb14..0dc773f 100644 --- a/reference/LearnerFcst.html +++ b/reference/LearnerFcst.html @@ -1,7 +1,7 @@ -Forecast Learner — LearnerFcst • mlr3forecast +Abstract class for forecast package learner — LearnerFcst • mlr3forecast Skip to contents @@ -40,20 +40,20 @@
-

Forecast Learner

+

Abstract class for forecast package learner

LearnerFcst.Rd
-

Forecast Learner

-

Forecast Learner

+

Abstract class for forecast package learner

+

Abstract class for forecast package learner

-

Super class

-

mlr3::Learner -> LearnerFcst

+

Super classes

+

mlr3::Learner -> mlr3::LearnerRegr -> LearnerFcst

Methods

diff --git a/reference/LearnerRegrForecast.html b/reference/LearnerRegrForecast.html index da82f3d..59add06 100644 --- a/reference/LearnerRegrForecast.html +++ b/reference/LearnerRegrForecast.html @@ -53,7 +53,7 @@

Abstract class for forecast package learner

Super classes

-

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrForecast

+

mlr3::Learner -> mlr3::LearnerRegr -> mlr3forecast::LearnerFcst -> LearnerRegrForecast

Methods

@@ -72,7 +72,7 @@

Public methodsmlr3::Learner$print()
  • mlr3::Learner$reset()
  • mlr3::Learner$train()
    • -
  • mlr3::LearnerRegr$initialize()
    • +
  • mlr3forecast::LearnerFcst$initialize()

    • Method clone()

    The objects of this class are cloneable with this method.

    diff --git a/reference/TaskFcst.html b/reference/TaskFcst.html index a10a90b..c864966 100644 --- a/reference/TaskFcst.html +++ b/reference/TaskFcst.html @@ -1,12 +1,12 @@ -Forecast Task — TaskFcst • mlr3forecast Skip to contents @@ -52,10 +52,10 @@

    Forecast Task

    -

    This task specializes Task and TaskSupervised for forecast problems. +

    This task specializes Task and TaskSupervised for regression problems. The target column is assumed to be numeric. -The task_type is set to "fcst".

    -

    It is recommended to use as_task_fcst() for construction. +The task_type is set to "regr".

    +

    It is recommended to use as_task_regr() for construction. Predefined tasks are stored in the dictionary mlr_tasks.

    @@ -80,15 +80,6 @@

    See also

    Super classes

    mlr3::Task -> mlr3::TaskSupervised -> mlr3::TaskRegr -> TaskFcst

    -

    -
    -

    Public fields

    -

    key
    -

    (character(1))
    -Key of data.

    - - -

    Methods

    @@ -121,16 +112,9 @@

    Public methods

    Method new()

    Creates a new instance of this R6 class. -The function as_task_fcst() provides an alternative way to construct forecast tasks.

    +The function as_task_regr() provides an alternative way to construct regression tasks.

    Usage

    -

    TaskFcst$new(
-  id,
-  backend,
-  target,
-  key = NULL,
-  label = NA_character_,
-  extra_args = list()
-)

    +

    TaskFcst$new(id, backend, target, label = NA_character_, extra_args = list())

    @@ -151,10 +135,6 @@

    ArgumentsArguments

    Examples

    - 
    airpassengers = tsbox::ts_dt(AirPassengers)
-data.table::setnames(airpassengers, c("date", "passengers"))
-task = as_task_fcst(airpassengers, target = "passengers", index = "date")
+    
    task = as_task_regr(palmerpenguins::penguins, target = "bill_length_mm")
 task$task_type
 #> [1] "regr"
 task$formula()
-#> passengers ~ .
+#> bill_length_mm ~ .
 #> NULL
 task$truth()
-#>   [1] 112 118 132 129 121 135 148 148 136 119 104 118 115 126 141 135 125 149
-#>  [19] 170 170 158 133 114 140 145 150 178 163 172 178 199 199 184 162 146 166
-#>  [37] 171 180 193 181 183 218 230 242 209 191 172 194 196 196 236 235 229 243
-#>  [55] 264 272 237 211 180 201 204 188 235 227 234 264 302 293 259 229 203 229
-#>  [73] 242 233 267 269 270 315 364 347 312 274 237 278 284 277 317 313 318 374
-#>  [91] 413 405 355 306 271 306 315 301 356 348 355 422 465 467 404 347 305 336
-#> [109] 340 318 362 348 363 435 491 505 404 359 310 337 360 342 406 396 420 472
-#> [127] 548 559 463 407 362 405 417 391 419 461 472 535 622 606 508 461 390 432
-task$data(rows = 1:3)
-#>    passengers       date
-#>         <num>     <Date>
-#> 1:        112 1949-01-01
-#> 2:        118 1949-02-01
-#> 3:        132 1949-03-01
+#>   [1] 39.1 39.5 40.3   NA 36.7 39.3 38.9 39.2 34.1 42.0 37.8 37.8 41.1 38.6 34.6
+#>  [16] 36.6 38.7 42.5 34.4 46.0 37.8 37.7 35.9 38.2 38.8 35.3 40.6 40.5 37.9 40.5
+#>  [31] 39.5 37.2 39.5 40.9 36.4 39.2 38.8 42.2 37.6 39.8 36.5 40.8 36.0 44.1 37.0
+#>  [46] 39.6 41.1 37.5 36.0 42.3 39.6 40.1 35.0 42.0 34.5 41.4 39.0 40.6 36.5 37.6
+#>  [61] 35.7 41.3 37.6 41.1 36.4 41.6 35.5 41.1 35.9 41.8 33.5 39.7 39.6 45.8 35.5
+#>  [76] 42.8 40.9 37.2 36.2 42.1 34.6 42.9 36.7 35.1 37.3 41.3 36.3 36.9 38.3 38.9
+#>  [91] 35.7 41.1 34.0 39.6 36.2 40.8 38.1 40.3 33.1 43.2 35.0 41.0 37.7 37.8 37.9
+#> [106] 39.7 38.6 38.2 38.1 43.2 38.1 45.6 39.7 42.2 39.6 42.7 38.6 37.3 35.7 41.1
+#> [121] 36.2 37.7 40.2 41.4 35.2 40.6 38.8 41.5 39.0 44.1 38.5 43.1 36.8 37.5 38.1
+#> [136] 41.1 35.6 40.2 37.0 39.7 40.2 40.6 32.1 40.7 37.3 39.0 39.2 36.6 36.0 37.8
+#> [151] 36.0 41.5 46.1 50.0 48.7 50.0 47.6 46.5 45.4 46.7 43.3 46.8 40.9 49.0 45.5
+#> [166] 48.4 45.8 49.3 42.0 49.2 46.2 48.7 50.2 45.1 46.5 46.3 42.9 46.1 44.5 47.8
+#> [181] 48.2 50.0 47.3 42.8 45.1 59.6 49.1 48.4 42.6 44.4 44.0 48.7 42.7 49.6 45.3
+#> [196] 49.6 50.5 43.6 45.5 50.5 44.9 45.2 46.6 48.5 45.1 50.1 46.5 45.0 43.8 45.5
+#> [211] 43.2 50.4 45.3 46.2 45.7 54.3 45.8 49.8 46.2 49.5 43.5 50.7 47.7 46.4 48.2
+#> [226] 46.5 46.4 48.6 47.5 51.1 45.2 45.2 49.1 52.5 47.4 50.0 44.9 50.8 43.4 51.3
+#> [241] 47.5 52.1 47.5 52.2 45.5 49.5 44.5 50.8 49.4 46.9 48.4 51.1 48.5 55.9 47.2
+#> [256] 49.1 47.3 46.8 41.7 53.4 43.3 48.1 50.5 49.8 43.5 51.5 46.2 55.1 44.5 48.8
+#> [271] 47.2   NA 46.8 50.4 45.2 49.9 46.5 50.0 51.3 45.4 52.7 45.2 46.1 51.3 46.0
+#> [286] 51.3 46.6 51.7 47.0 52.0 45.9 50.5 50.3 58.0 46.4 49.2 42.4 48.5 43.2 50.6
+#> [301] 46.7 52.0 50.5 49.5 46.4 52.8 40.9 54.2 42.5 51.0 49.7 47.5 47.6 52.0 46.9
+#> [316] 53.5 49.0 46.2 50.9 45.5 50.9 50.8 50.1 49.0 51.5 49.8 48.1 51.4 45.7 50.7
+#> [331] 42.5 52.2 45.2 49.3 50.2 45.6 51.9 46.8 45.7 55.8 43.5 49.6 50.8 50.2
+task$data(rows = 1:3, cols = task$feature_names[1:2])
+#>    bill_depth_mm body_mass_g
+#>            <num>       <int>
+#> 1:          18.7        3750
+#> 2:          17.4        3800
+#> 3:          18.0        3250