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| Original file line number | Diff line number | Diff line change |
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| #' @title (CAST) Repeated K-fold Nearest Neighbour Distance Matching | ||
| #' | ||
| #' @template rox_sptcv_knndm | ||
| #' @name mlr_resamplings_repeated_spcv_knndm | ||
| #' | ||
| #' @section Parameters: | ||
| #' | ||
| #' * `repeats` (`integer(1)`)\cr | ||
| #' Number of repeats. | ||
| #' | ||
| #' @references | ||
| #' `r format_bib("linnenbrink2023")` | ||
| #' | ||
| #' @export | ||
| #' @examples | ||
| #' library(mlr3) | ||
| #' library(mlr3spatial) | ||
| #' set.seed(42) | ||
| #' simarea = list(matrix(c(0, 0, 0, 100, 100, 100, 100, 0, 0, 0), ncol = 2, byrow = TRUE)) | ||
| #' simarea = sf::st_polygon(simarea) | ||
| #' train_points = sf::st_sample(simarea, 1000, type = "random") | ||
| #' train_points = sf::st_as_sf(train_points) | ||
| #' train_points$target = as.factor(sample(c("TRUE", "FALSE"), 1000, replace = TRUE)) | ||
| #' pred_points = sf::st_sample(simarea, 1000, type = "regular") | ||
| #' | ||
| #' task = mlr3spatial::as_task_classif_st(sf::st_as_sf(train_points), "target", positive = "TRUE") | ||
| #' | ||
| #' cv_knndm = rsmp("repeated_spcv_knndm", ppoints = pred_points, repeats = 2) | ||
| #' cv_knndm$instantiate(task) | ||
|
|
||
| #' #' ### Individual sets: | ||
| #' # cv_knndm$train_set(1) | ||
| #' # cv_knndm$test_set(1) | ||
| #' # check that no obs are in both sets | ||
| #' intersect(cv_knndm$train_set(1), cv_knndm$test_set(1)) # good! | ||
| #' | ||
| #' # Internal storage: | ||
| #' # cv_knndm$instance # table | ||
| ResamplingRepeatedSpCVKnndm = R6Class("ResamplingRepeatedSpCVKnndm", | ||
| inherit = mlr3::Resampling, | ||
| public = list( | ||
| #' @description | ||
| #' Create a "K-fold Nearest Neighbour Distance Matching" resampling instance. | ||
| #' | ||
| #' @param id `character(1)`\cr | ||
| #' Identifier for the resampling strategy. | ||
| initialize = function(id = "repeated_spcv_knndm") { | ||
| ps = ParamSet$new(params = list( | ||
| ParamUty$new("modeldomain", default = NULL, | ||
| custom_check = function(x) { | ||
| checkmate::check_class(x, "SpatRaster", | ||
| null.ok = TRUE) | ||
| } | ||
| ), | ||
| ParamUty$new("ppoints", default = NULL, | ||
| custom_check = function(x) { | ||
| checkmate::check_class(x, "sfc_POINT", | ||
| null.ok = TRUE) | ||
| } | ||
| ), | ||
| ParamFct$new("space", levels = "geographical", default = "geographical"), | ||
| ParamInt$new("folds", default = 10, lower = 2), | ||
| ParamDbl$new("maxp", default = 0.5, lower = 0, upper = 1), | ||
| ParamFct$new("clustering", default = "hierarchical", | ||
| levels = c("hierarchical", "kmeans"), tags = "required"), | ||
| ParamUty$new("linkf", default = "ward.D2"), | ||
| ParamInt$new("samplesize"), | ||
| ParamFct$new("sampling", levels = c("random", "hexagonal", "regular", "Fibonacci")), | ||
| ParamInt$new("repeats", lower = 1, default = 1L, tags = "required") | ||
| )) | ||
| ps$values = list(repeats = 1, folds = 10) | ||
|
|
||
| super$initialize( | ||
| id = id, | ||
| param_set = ps, | ||
| label = "Repeated Spatial 'K-fold Nearest Neighbour Distance Matching", | ||
| man = "mlr3spatiotempcv::mlr_resamplings_repeated_spcv_knndm" | ||
| ) | ||
| }, | ||
|
|
||
| #' @description Translates iteration numbers to fold number. | ||
| #' @param iters `integer()`\cr | ||
| #' Iteration number. | ||
| folds = function(iters) { | ||
| iters = assert_integerish(iters, any.missing = FALSE, coerce = TRUE) | ||
| ((iters - 1L) %% as.integer(self$param_set$values$folds)) + 1L | ||
| }, | ||
|
|
||
| #' @description Translates iteration numbers to repetition number. | ||
| #' @param iters `integer()`\cr | ||
| #' Iteration number. | ||
| repeats = function(iters) { | ||
| iters = assert_integerish(iters, any.missing = FALSE, coerce = TRUE) | ||
| ((iters - 1L) %/% as.integer(self$param_set$values$folds)) + 1L | ||
| }, | ||
|
|
||
| #' @description | ||
| #' Materializes fixed training and test splits for a given task. | ||
| #' @param task [Task]\cr | ||
| #' A task to instantiate. | ||
| instantiate = function(task) { | ||
|
|
||
| mlr3::assert_task(task) | ||
| assert_spatial_task(task) | ||
| groups = task$groups | ||
|
|
||
| pv = self$param_set$values | ||
|
|
||
| # Set values to default if missing | ||
| mlr3misc::map( | ||
| c("modeldomain", "ppoints", "space", "maxp", "folds", "clustering", | ||
| "linkf", "samplesize", "sampling"), | ||
| function(x) private$.set_default_param_values(x) | ||
| ) | ||
|
|
||
| if (is.null(pv$modeldomain) && is.null(pv$ppoints)) { | ||
| stopf("Either 'modeldomain' or 'ppoints' need to be set.") | ||
| } | ||
|
|
||
| if (!is.null(groups)) { | ||
| stopf("Grouping is not supported for spatial resampling methods.") | ||
| } | ||
|
|
||
| if (!is.null(groups)) { | ||
| stopf("Grouping is not supported for spatial resampling methods") | ||
| } | ||
|
|
||
| instance = private$.sample( | ||
| task$row_ids, | ||
| task$coordinates(), | ||
| task$crs | ||
| ) | ||
|
|
||
| self$instance = instance | ||
| self$task_hash = task$hash | ||
| self$task_nrow = task$nrow | ||
| invisible(self) | ||
| } | ||
| ), | ||
| active = list( | ||
|
|
||
| #' @field iters `integer(1)`\cr | ||
| #' Returns the number of resampling iterations, depending on the | ||
| #' values stored in the `param_set`. | ||
| iters = function() { | ||
| pv = self$param_set$values | ||
| # hack for autoplot | ||
| if (!is.null(names(self$instance))) { | ||
| as.integer(pv$repeats) * as.integer(length(self$instance$train)) | ||
| } else { | ||
| as.integer(pv$repeats) * as.integer(length(self$instance[[1]]$test)) | ||
| } | ||
| } | ||
| ), | ||
| private = list( | ||
| .sample = function(ids, coords, crs) { | ||
|
|
||
| points = sf::st_as_sf(coords, | ||
| coords = colnames(coords), | ||
| crs = crs | ||
| ) | ||
|
|
||
| pv = self$param_set$values | ||
| map(seq_len(pv$repeats), function(i) { | ||
| inds = CAST::knndm(tpoints = points, | ||
| modeldomain = self$param_set$values$modeldomain, | ||
| ppoints = self$param_set$values$ppoints, | ||
| k = self$param_set$values$folds, | ||
| maxp = self$param_set$values$maxp, | ||
| clustering = self$param_set$values$clustering, | ||
| linkf = self$param_set$values$linkf, | ||
| samplesize = self$param_set$values$samplesize, | ||
| sampling = self$param_set$values$sampling) | ||
|
|
||
| list(train = inds$indx_train, test = inds$indx_test) | ||
| }) | ||
|
|
||
| }, | ||
| .set_default_param_values = function(param) { | ||
| if (is.null(self$param_set$values[[param]])) { | ||
| self$param_set$values[[param]] = self$param_set$default[[param]] | ||
| } | ||
| }, | ||
| .get_train = function(i) { | ||
| i = as.integer(i) - 1L | ||
| folds = as.integer(self$param_set$values$folds) | ||
| rep = i %/% folds + 1L | ||
| fold = i %% folds + 1L | ||
| self$instance[[rep]]$train[[fold]] | ||
| }, | ||
| .get_test = function(i) { | ||
| i = as.integer(i) - 1L | ||
| folds = as.integer(self$param_set$values$folds) | ||
| rep = i %/% folds + 1L | ||
| fold = i %% folds + 1L | ||
| self$instance[[rep]]$test[[fold]] | ||
| } | ||
| ) | ||
| ) |
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