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ResamplingVariableSizeCV
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,7 @@ | ||
| Package: mlr3resampling | ||
| Type: Package | ||
| Title: Resampling Algorithms for 'mlr3' Framework | ||
| Version: 2023.12.23 | ||
| Version: 2023.12.28 | ||
| Authors@R: c( | ||
| person("Toby", "Hocking", | ||
| email="[email protected]", | ||
|
|
@@ -52,6 +52,9 @@ Description: A supervised learning algorithm inputs a train set, | |
| For more information, | ||
| <https://tdhock.github.io/blog/2023/R-gen-new-subsets/> | ||
| describes the method in depth. | ||
| How many train samples are required to get accurate predictions on a | ||
| test set? Cross-validation can be used to answer this question, with | ||
| variable size train sets. | ||
| License: GPL-3 | ||
| URL: https://github.com/tdhock/mlr3resampling | ||
| BugReports: https://github.com/tdhock/mlr3resampling/issues | ||
|
|
||
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| @@ -1,3 +1,3 @@ | ||
| import(R6, checkmate, data.table, mlr3, mlr3misc, paradox) | ||
| export(ResamplingSameOtherCV, score) | ||
| export(ResamplingSameOtherCV, score, ResamplingVariableSizeTrainCV) | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,83 @@ | ||
| ResamplingBase = R6::R6Class( | ||
| "Resampling", | ||
| public = list( | ||
| id = NULL, | ||
| label = NULL, | ||
| param_set = NULL, | ||
| instance = NULL, | ||
| task_hash = NA_character_, | ||
| task_nrow = NA_integer_, | ||
| duplicated_ids = NULL, | ||
| man = NULL, | ||
| initialize = function(id, param_set = ps(), duplicated_ids = FALSE, label = NA_character_, man = NA_character_) { | ||
| self$id = checkmate::assert_string(id, min.chars = 1L) | ||
| self$label = checkmate::assert_string(label, na.ok = TRUE) | ||
| self$param_set = paradox::assert_param_set(param_set) | ||
| self$duplicated_ids = checkmate::assert_flag(duplicated_ids) | ||
| self$man = checkmate::assert_string(man, na.ok = TRUE) | ||
| }, | ||
| format = function(...) { | ||
| sprintf("<%s>", class(self)[1L]) | ||
| }, | ||
| print = function(...) { | ||
| cat( | ||
| format(self), | ||
| if (is.null(self$label) || is.na(self$label)) | ||
| "" else paste0(": ", self$label) | ||
| ) | ||
| cat("\n* Iterations:", self$iters) | ||
| cat("\n* Instantiated:", self$is_instantiated) | ||
| cat("\n* Parameters:\n") | ||
| str(self$param_set$values) | ||
| }, | ||
| help = function() { | ||
| self$man | ||
| }, | ||
| train_set = function(i) { | ||
| self$instance$iteration.dt$train[[i]] | ||
| }, | ||
| test_set = function(i) { | ||
| self$instance$iteration.dt$test[[i]] | ||
| } | ||
| ), | ||
| active = list( | ||
| iters = function(rhs) { | ||
| nrow(self$instance$iteration.dt) | ||
| }, | ||
| is_instantiated = function(rhs) { | ||
| !is.null(self$instance) | ||
| }, | ||
| hash = function(rhs) { | ||
| if (!self$is_instantiated) { | ||
| return(NA_character_) | ||
| } | ||
| mlr3misc::calculate_hash(list( | ||
| class(self), | ||
| self$id, | ||
| self$param_set$values, | ||
| self$instance)) | ||
| } | ||
| ), | ||
| private = list( | ||
| .sample = function(ids, ...) { | ||
| data.table( | ||
| row_id = ids, | ||
| fold = sample( | ||
| seq(0, length(ids)-1) %% | ||
| as.integer(self$param_set$values$folds) + 1L | ||
| ), | ||
| key = "fold" | ||
| ) | ||
| }, | ||
| .combine = function(instances) { | ||
| rbindlist(instances, use.names = TRUE) | ||
| }, | ||
| deep_clone = function(name, value) { | ||
| switch(name, | ||
| "instance" = copy(value), | ||
| "param_set" = value$clone(deep = TRUE), | ||
| value | ||
| ) | ||
| } | ||
| ) | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| ResamplingVariableSizeTrainCV = R6::R6Class( | ||
| "ResamplingVariableSizeTrainCV", | ||
| inherit=ResamplingBase, | ||
| public = list( | ||
| initialize = function() { | ||
| ps = paradox::ps( | ||
| folds = paradox::p_int(2L, tags = "required"), | ||
| min_train_data=paradox::p_int(1L, tags = "required"), | ||
| random_seeds=paradox::p_int(1L, tags = "required"), | ||
| train_sizes = paradox::p_int(2L, tags = "required")) | ||
| ps$values = list( | ||
| folds = 3L, | ||
| min_train_data=10L, | ||
| random_seeds=3L, | ||
| train_sizes=5L) | ||
| super$initialize( | ||
| id = "variable_size_train_cv", | ||
| param_set = ps, | ||
| label = "Cross-Validation with variable size train sets", | ||
| man = "ResamplingVariableSizeTrainCV") | ||
| }, | ||
| instantiate = function(task) { | ||
| task = mlr3::assert_task(mlr3::as_task(task)) | ||
| reserved.names <- c( | ||
| "row_id", "fold", "group", "display_row", | ||
| "train.groups", "test.fold", "test.group", "iteration", | ||
| "test", "train", "algorithm", "uhash", "nr", "task", "task_id", | ||
| "learner", "learner_id", "resampling", "resampling_id", | ||
| "prediction") | ||
| ## bad.names <- group.name.vec[group.name.vec %in% reserved.names] | ||
| ## if(length(bad.names)){ | ||
| ## first.bad <- bad.names[1] | ||
| ## stop(sprintf("col with role group must not be named %s; please fix by renaming %s col", first.bad, first.bad)) | ||
| ## } | ||
| ## orig.group.dt <- task$data(cols=group.name.vec) | ||
| strata <- if(is.null(task$strata)){ | ||
| data.dt <- task$data() | ||
| data.table(N=nrow(data.dt), row_id=list(1:nrow(data.dt))) | ||
| }else task$strata | ||
| folds = private$.combine( | ||
| lapply(strata$row_id, private$.sample, task = task) | ||
| )[order(row_id)] | ||
| min_train_data <- self$param_set$values[["min_train_data"]] | ||
| if(task$nrow <= min_train_data){ | ||
| stop(sprintf( | ||
| "task$nrow=%d but should be larger than min_train_data=%d", | ||
| task$nrow, min_train_data)) | ||
| } | ||
| uniq.folds <- sort(unique(folds$fold)) | ||
| iteration.dt.list <- list() | ||
| for(test.fold in uniq.folds){ | ||
| is.set.fold <- list( | ||
| test=folds[["fold"]] == test.fold) | ||
| is.set.fold[["train"]] <- !is.set.fold[["test"]] | ||
| i.set.list <- lapply(is.set.fold, which) | ||
| max_train_data <- length(i.set.list$train) | ||
| log.range.data <- log(c(min_train_data, max_train_data)) | ||
| seq.args <- c(as.list(log.range.data), list(l=self$param_set$values[["train_sizes"]])) | ||
| log.train.sizes <- do.call(seq, seq.args) | ||
| train.size.vec <- unique(as.integer(round(exp(log.train.sizes)))) | ||
| for(seed in 1:self$param_set$values[["random_seeds"]]){ | ||
| set.seed(seed) | ||
| ord.i.vec <- sample(i.set.list$train) | ||
| iteration.dt.list[[paste(test.fold, seed)]] <- data.table( | ||
| test.fold, | ||
| seed, | ||
| train_size=train.size.vec, | ||
| train=lapply(train.size.vec, function(last)ord.i.vec[1:last]), | ||
| test=list(i.set.list$test)) | ||
| } | ||
| } | ||
| self$instance <- list( | ||
| iteration.dt=rbindlist(iteration.dt.list)[, iteration := .I][], | ||
| id.dt=folds) | ||
| self$task_hash = task$hash | ||
| self$task_nrow = task$nrow | ||
| invisible(self) | ||
| } | ||
| ) | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,14 @@ | ||
| score <- function(bench.result, ...){ | ||
| algorithm <- learner_id <- NULL | ||
| ## Above to avoid CRAN NOTE. | ||
| bench.score <- bench.result$score(...) | ||
| out.dt.list <- list() | ||
| for(score.i in 1:nrow(bench.score)){ | ||
| bench.row <- bench.score[score.i] | ||
| it.dt <- bench.row$resampling[[1]]$instance$iteration.dt | ||
| out.dt.list[[score.i]] <- it.dt[ | ||
| bench.row, on="iteration" | ||
| ][, algorithm := sub(".*[.]", "", learner_id)] | ||
| } | ||
| rbindlist(out.dt.list) | ||
| } |
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