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feat: add partition implementation
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‎DESCRIPTION‎

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@@ -10,7 +10,7 @@ URL: https://mlr3forecast.mlr-org.com,
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https://github.com/mlr-org/mlr3forecast
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BugReports: https://github.com/mlr-org/mlr3forecast/issues
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Depends:
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mlr3 (>= 1.1.0),
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mlr3 (>= 1.1.0.9000),
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R (>= 3.3.0)
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Imports:
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backports,
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xts,
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zoo
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Remotes:
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mlr-org/mlr3,
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mlr-org/mlr3pipelines
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Config/testthat/edition: 3
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Encoding: UTF-8
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'assertions.R'
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'autoplot.R'
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'bibentries.R'
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'partition.R'
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'reexports.R'
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'tsf.R'
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'utils.R'

‎NAMESPACE‎

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@@ -11,6 +11,7 @@ S3method(as_task_fcst,zoo)
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S3method(as_tasks_fcst,default)
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S3method(as_tasks_fcst,list)
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S3method(autoplot,TaskFcst)
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S3method(partition,TaskFcst)
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S3method(plot,TaskFcst)
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export(ForecastLearner)
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export(ForecastLearnerManual)

‎R/ResamplingFcstHoldout.R‎

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@@ -90,10 +90,12 @@ ResamplingFcstHoldout = R6Class(
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nr = max(n_obs + n, 0L)
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}
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order_cols = task$col_roles$order
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key_cols = task$col_roles$key
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col_roles = task$col_roles
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order_cols = col_roles$order
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key_cols = col_roles$key
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has_key_cols = length(key_cols) > 0L
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dt = task$backend$data(rows = ids, cols = c(task$backend$primary_key, order_cols, key_cols))
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if (has_key_cols) {
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setnames(dt, "..row_id", "row_id")
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setorderv(dt, c(key_cols, order_cols))
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} else {
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setnames(dt, c("row_id", "order"))
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setorderv(dt, "order")
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list(train = dt[1:nr, row_id], test = dt[(nr + 1L):.N, row_id])
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list(
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train = dt[1:nr, row_id],
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test = if (nrow(dt) > nr) dt[(nr + 1L):.N, row_id] else integer()
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)
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}
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},
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‎R/partition.R‎

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#' @export
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partition.TaskFcst = function(task, ratio = 0.67) {
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task = task$clone(deep = TRUE)
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if (sum(ratio) >= 1) {
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stopf("Sum of 'ratio' must be smaller than 1")
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}
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if (length(ratio) == 1L) {
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ratio[2L] = 1 - ratio
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} else {
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ratio[3L] = 1 - (ratio[1L] + ratio[2L])
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}
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r1 = rsmp("fcst.holdout", ratio = ratio[1L])$instantiate(task)
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task$row_roles$use = r1$test_set(1L)
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r2 = rsmp("fcst.holdout", ratio = ratio[2L] / (1 - ratio[1L]))$instantiate(task)
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list(
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train = r1$train_set(1L),
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test = r2$train_set(1L),
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validation = r2$test_set(1L)
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)
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}

‎man-roxygen/example.R‎

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#' task = tsk("airpassengers")
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#'
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#' # Create train and test set
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#' resampling = rsmp("fcst.holdout", ratio = 0.7)$instantiate(task)
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#' ids = partition(task)
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#'
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#' # Train the learner on the training ids
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#' learner$train(task, row_ids = resampling$train_set(1))
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#' learner$train(task, row_ids = ids$train)
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#'
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#' # Print the model
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#' print(learner$model)
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#' if ("importance" %in% learner$properties) print(learner$importance)
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#'
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#' # Make predictions for the test rows
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#' predictions = learner$predict(task, row_ids = resampling$test_set(1))
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#' predictions = learner$predict(task, row_ids = ids$test)
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#'
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#' # Score the predictions
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#' predictions$score()

‎man/mlr_learners_fcst.adam.Rd‎

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‎man/mlr_learners_fcst.arfima.Rd‎

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‎man/mlr_learners_fcst.arima.Rd‎

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‎man/mlr_learners_fcst.auto_adam.Rd‎

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‎man/mlr_learners_fcst.auto_arima.Rd‎

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