From 561d6af5174b9399af00088c8acd585d3b4b66b8 Mon Sep 17 00:00:00 2001 From: Cesaire Joris Kuete Fouodo Date: Tue, 26 Nov 2024 20:46:50 +0100 Subject: [PATCH] Fix first testing issues --- DESCRIPTION | 2 +- NAMESPACE | 1 - R/BestSpecificLearner.R | 28 +++--- R/HashTable.R | 2 +- R/Lrner.R | 5 + R/Testing.R | 51 +++++++++- R/TrainData.R | 18 +++- R/Training.R | 94 ++++++++++++------ R/VarSel.R | 22 ++++- R/predict.bestSpecificLearner.R | 2 +- R/predict.weightedMeanLearner.R | 1 + R/testingFunctions.R | 16 ++- R/trainingFunctions.R | 104 ++++++++++---------- R/weightedMeanLearner.R | 4 +- README_files/figure-gfm/upsetplot-1.png | Bin 21606 -> 19819 bytes README_files/figure-gfm/upsetplot_new-1.png | Bin 21536 -> 18823 bytes data/multi_omics.rda | Bin 0 -> 365769 bytes man/HashTable.Rd | 2 +- man/Training.Rd | 48 ++++++--- man/VarSel.Rd | 12 ++- man/createTesting.Rd | 5 +- man/createTrainLayer.Rd | 4 +- man/createTrainMetaLayer.Rd | 6 +- man/fusemlr.Rd | 10 +- man/multi_omics.Rd | 29 ++++++ man/varSelection.Rd | 5 +- tests/testthat/test-Data.R | 4 +- tests/testthat/test-Lrner.R | 2 +- tests/testthat/test-Model.R | 8 +- tests/testthat/test-TestData.R | 10 +- tests/testthat/test-TrainData.R | 24 ++--- tests/testthat/test-TrainLayer.R | 8 +- tests/testthat/test-TrainMetaLayer.R | 6 +- tests/testthat/test-Training.R | 24 ++--- tests/testthat/test-VarSel.R | 21 ++-- tests/testthat/test-bestSpecificLearner.R | 2 +- tests/testthat/test-mutli_omics.R | 3 + tests/testthat/test-weightedMeanLearner.R | 2 +- vignettes/how_to_use.Rmd | 68 +++++++------ 39 files changed, 415 insertions(+), 238 deletions(-) create mode 100644 data/multi_omics.rda create mode 100644 man/multi_omics.Rd create mode 100644 tests/testthat/test-mutli_omics.R diff --git a/DESCRIPTION b/DESCRIPTION index 825536b..3c3757b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -44,7 +44,7 @@ Collate: 'TrainMetaLayer.R' 'Training.R' 'VarSel.R' - 'entities.R' + 'multi_omics.R' 'weightedMeanLearner.R' 'predict.bestSpecificLearner.R' 'predict.weightedMeanLearner.R' diff --git a/NAMESPACE b/NAMESPACE index aaeb327..594196e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -32,7 +32,6 @@ export(createTraining) export(extractData) export(extractModel) export(fusemlr) -export(removeLayer) export(upsetplot) export(varSelection) export(weightedMeanLearner) diff --git a/R/BestSpecificLearner.R b/R/BestSpecificLearner.R index 53f3201..99acb53 100644 --- a/R/BestSpecificLearner.R +++ b/R/BestSpecificLearner.R @@ -23,27 +23,31 @@ #' my_best_model = bestSpecificLearner(x = x, y = y) #' bestSpecificLearner = function (x, y, perf = NULL) { + if (!is.data.frame(x)) { + stop("x must be a data.frame.") + } if (is.null(perf)) { - # y must be binomial for Brier Score estimation. - # If dichotomy, first category (case) = 1 and - # second (control) = 0 - if ((length(unique(y)) > 2) | is.character(y)) { - stop("y must be either binary or two level factor variable.") + if (is.numeric(y) & (length(unique(y)) > 2)) { + perf_values = lapply(X = x, FUN = function (predicted) { + mean(x = (predicted - y)^2, na.rm = TRUE) + }) } else { - if (!all(y %in% 0:1)) { - y = 1 - as.integer(y) + if ((length(unique(y)) > 2) | is.character(y)) { + stop("y must be either binary or two level factor variable.\n") } else { if (is.factor(y)) { y = as.integer(y) - 1 } else { - y = y + if (!all(y %in% 0:1)) { + stop("y must take its values between 0 and 1.\n") + } } + perf_values = lapply(X = x, FUN = function (predicted) { + mean(x = (predicted - y)^2, na.rm = TRUE) + }) } - perf_values = lapply(X = x, FUN = function (predicted) { - mean(x = (predicted - y)^2L, na.rm = TRUE) - }) - perf_values = unlist(perf_values) } + perf_values = unlist(perf_values) } else { # nocov start if (is.function(perf)) { diff --git a/R/HashTable.R b/R/HashTable.R index 3fce809..46ef32b 100644 --- a/R/HashTable.R +++ b/R/HashTable.R @@ -1,5 +1,5 @@ #' @title Class HashTable -#' @description Hashtable to contain object entities. Storage objects like [Training] and [TrainLayer] are extensions of this class. +#' @description Hashtable to contain object modalities. Storage objects like [Training] and [TrainLayer] are extensions of this class. #' #' @export #' @importFrom R6 R6Class diff --git a/R/Lrner.R b/R/Lrner.R index e23d5a0..bb59e62 100644 --- a/R/Lrner.R +++ b/R/Lrner.R @@ -42,6 +42,11 @@ Lrner <- R6Class("Lrner", private$lrn_fct = lrn_fct private$param_train = param_train_list private$param_pred = param_pred_list + if (is.null(package)) { + if (!(exists(lrn_fct, envir = .GlobalEnv, inherits = TRUE) | is.function(get(lrn_fct, envir = .GlobalEnv)))) { + stop(sprintf("Function %s does not exists.\n Maybe you forget to specify its package?", lrn_fct)) + } + } if (!any(c("TrainLayer", "TrainMetaLayer") %in% class(train_layer))) { stop("A Lrner can only belong to a TrainLayer or a TrainMetaLayer object.") } diff --git a/R/Testing.R b/R/Testing.R index 6f75c7b..9798144 100644 --- a/R/Testing.R +++ b/R/Testing.R @@ -23,9 +23,25 @@ Testing <- R6Class("Testing", #' Testing id. #' @param ind_col (`character(1)`) #' Name of column of individuals IDS in testing data.frame. - initialize = function (id, ind_col) { + #' @param verbose (`boolean`) \cr + #' Warning messages will be displayed if set to TRUE. + initialize = function (id, ind_col, verbose = TRUE) { super$initialize(id = id) private$ind_col = ind_col + layers = self$getKeyClass() + layers = layers[layers$class %in% "TestLayer", ] + nb_layers = nrow(layers) + if (nb_layers) { + layer_dims = NULL + for (k in layers$key) { + layer = self$getFromHashTable(key = k) + test_data = layer$getTestData() + layer_dim = ncol(test_data$getData()) + layer_dims = c(layer_dims, layer_dim) + } + layer_dims = paste0(layer_dims, collapse = " | ") + cat(sprintf("p : %s\n", layer_dims)) + } }, #' @description #' Printer @@ -36,6 +52,26 @@ Testing <- R6Class("Testing", nb_layers = length(private$hash_table) cat(sprintf("Testing : %s\n", private$id)) cat(sprintf("Number of layers: %s\n", nb_layers)) + layers = self$getKeyClass() + layers = layers[layers$class %in% "TestLayer", ] + nb_layers = nrow(layers) + if (nb_layers) { + layer_ps = NULL + layer_ns = NULL + for (k in layers$key) { + layer = self$getFromHashTable(key = k) + test_data = layer$getTestData() + layer_p = ncol(test_data$getData()) + layer_n = nrow(test_data$getData()) + max_width <- max(nchar(layer_p), nchar(layer_n)) + layer_ps = c(layer_ps, format(layer_p, width = max_width, justify = "left")) + layer_ns = c(layer_ns, format(layer_n, width = max_width, justify = "left")) + } + layer_ps = paste0(layer_ps, collapse = " | ") + layer_ns = paste0(layer_ns, collapse = " | ") + cat(sprintf("p : %s\n", layer_ps)) + cat(sprintf("n : %s\n", layer_ns)) + } }, #' @description #' Gather individual IDs from all layer. @@ -81,6 +117,12 @@ Testing <- R6Class("Testing", return(private$ind_col) }, #' @description + #' Getter of the verbose setting. + #' @export + getVerbose = function () { + return(private$verbose) + }, + #' @description #' UpSet plot to show an overview of the overlap of individuals across various layers. #' #' @param ... \cr @@ -93,8 +135,8 @@ Testing <- R6Class("Testing", # This code accesses each layer (except TrainMetaLayer) level # and get the individual IDs. layers = layers[layers$class %in% "TestLayer", ] - if (!nrow(layers)) { - stop("No available layer in this testing object.") + if (!nrow(layers) | (nrow(layers) == 1L)) { + stop("No or only one available layer in this training object.") } ids_list = lapply(layers$key, function (k) { layer = self$getFromHashTable(key = k) @@ -129,7 +171,8 @@ Testing <- R6Class("Testing", } ), private = list( - ind_col = character(0L) + ind_col = character(0L), + verbose = TRUE ), cloneable = FALSE ) diff --git a/R/TrainData.R b/R/TrainData.R index 2b396b8..84fd4ea 100644 --- a/R/TrainData.R +++ b/R/TrainData.R @@ -26,6 +26,9 @@ TrainData <- R6Class("TrainData", if (!any(c("TrainLayer", "TrainMetaLayer") %in% class(train_layer))) { stop("A Traindata can only belong a TrainLayer or a TrainMetaLayer object.\n") } + if (!is.data.frame(data_frame)) { + stop("data_frame must be a data.frame.\n") + } target_obj = train_layer$getTargetObj() target_df = target_obj$getData() ind_col = train_layer$getTraining()$getIndCol() @@ -39,6 +42,19 @@ TrainData <- R6Class("TrainData", } data_frame[ , target] = NULL } + if (!all(data_frame[ , ind_col] %in% target_df[ , ind_col])) { + cat("The following IDs do not exist in target:\n") + id_not_in = !(data_frame[ , ind_col] %in% target_df[ , ind_col]) + cat(data_frame[id_not_in , ind_col], "\n") + if (length(id_not_in) == nrow(data_frame)) { + stop("None of the provided IDs have a match in the target.\n") + } else { + if (length(id_not_in) <= nrow(data_frame)) { + data_frame = data_frame[!(data_frame[ , ind_col] %in% id_not_in), ] + warning("Not matching ID(s) removed:\n") + } + } + } if (train_layer$checkTrainDataExist()) { # Remove TrainData if already existing key_class = train_layer$getKeyClass() @@ -77,7 +93,7 @@ TrainData <- R6Class("TrainData", .class = "TrainData") } if (any(missing_target)) { - warning(sprintf("%s individual(s) with missing target value(s) recognized and removed\n", + warning(sprintf("%s individual(s) with missing target value(s) recognized and removed.\n", sum(missing_target))) } if (any(missing_id)) { diff --git a/R/Training.R b/R/Training.R index 12ba687..ef17e8a 100644 --- a/R/Training.R +++ b/R/Training.R @@ -45,6 +45,9 @@ Training <- R6Class("Training", target_df, problem_type = "classification", verbose = TRUE) { + if (!is.logical(verbose)) { + stop("'verbose' must be bolean.") + } if (!is.character(id)) { stop("id must be a string") } @@ -68,17 +71,18 @@ Training <- R6Class("Training", stop("The problem type must be either classification or regression") } else { target_values = target_df[ , target, drop = TRUE] - uniq_target_values = length(unique(target_values)) + uniq_target_values = length(unique(target_values[complete.cases(target_values)])) if ((problem_type == "classification") & (uniq_target_values > 2L)) { - warning("You set up a classification problem for more than two classes.") + warning("You set up a classification problem for more than two classes.\n") } if ((problem_type == "regression") & (uniq_target_values == 2L)) { - warning("You set up a regression problem for only two classes.") + warning("You set up a regression problem for only two classes.\n") } } super$initialize(id = id) private$ind_col = ind_col private$target = target + private$verbose = TRUE private$target_obj = Target$new(id = "target", data_frame = target_df[ , c(ind_col, target)], training = self) @@ -97,11 +101,35 @@ Training <- R6Class("Training", status = "Trained" } cat(sprintf("Training : %s\n", private$id)) + cat(sprintf("Problem typ : %s\n", private$problem_typ)) cat(sprintf("Status : %s\n", status)) cat(sprintf("Number of layers: %s\n", nb_layers)) - # TODO: Fix the number of layer trained. cat(sprintf("Layers trained : %s\n", private$nb_trained_layer)) - cat(sprintf("n : %s\n", nrow(private$target_obj$getData()))) + # cat(sprintf("n : %s\n", nrow(private$target_obj$getData()))) + layers = self$getKeyClass() + if (private$status) { + layers = layers[layers$class %in% c("TrainLayer", "TrainMetaLayer"), ] + } else { + layers = layers[layers$class %in% "TrainLayer", ] + } + nb_layers = nrow(layers) + if (nb_layers) { + layer_ps = NULL + layer_ns = NULL + for (k in layers$key) { + layer = self$getFromHashTable(key = k) + train_data = layer$getTrainData() + layer_p = ncol(train_data$getData()) + layer_n = nrow(train_data$getData()) + max_width <- max(nchar(layer_p), nchar(layer_n)) + layer_ps = c(layer_ps, format(layer_p, width = max_width, justify = "left")) + layer_ns = c(layer_ns, format(layer_n, width = max_width, justify = "left")) + } + layer_ps = paste0(layer_ps, collapse = " | ") + layer_ns = paste0(layer_ns, collapse = " | ") + cat(sprintf("p : %s\n", layer_ps)) + cat(sprintf("n : %s\n", layer_ns)) + } }, #' @description #' Train each layer of the current Training. @@ -110,16 +138,13 @@ Training <- R6Class("Training", #' Subset of individuals IDs to be used for training. #' @param use_var_sel `boolean(1)` \cr #' If TRUE, selected variables available at each layer are used. - #' @param verbose (`boolean`) \cr - #' Warning messages will be displayed if set to TRUE. #' @return #' Returns the object itself, with a model for each layer. #' @export #' #' trainLayer = function (ind_subset = NULL, - use_var_sel = FALSE, - verbose = TRUE) { + use_var_sel = FALSE) { layers = self$getKeyClass() nb_layers = nrow(layers[layers$class %in% "TrainLayer", ]) if (nb_layers) { @@ -129,8 +154,7 @@ Training <- R6Class("Training", for (k in layers$key) { layer = self$getFromHashTable(key = k) layer$train(ind_subset = ind_subset, - use_var_sel = use_var_sel, - verbose = verbose) + use_var_sel = use_var_sel) } } else { stop("No existing layer in the current training object.") @@ -181,8 +205,6 @@ Training <- R6Class("Training", #' If TRUE, selected variables available at each layer are used. #' @param impute (`boolean`) \cr #' If TRUE, mode or median based imputation is performed on the modality-specific predictions. - #' @param verbose (`boolean`) \cr - #' Warning messages will be displayed if set to TRUE. #' #' @return #' The current object is returned, with a meta training dataset assigned to the meta layer. @@ -191,8 +213,8 @@ Training <- R6Class("Training", createMetaTrainData = function (resampling_method, resampling_arg, use_var_sel, - impute = TRUE, - verbose = TRUE) { + impute = TRUE) { + verbose = self$getVerbose() layers = self$getKeyClass() if (!("TrainMetaLayer" %in% layers$class)) { stop("No existing meta layer. I cannot create meta training data.") @@ -204,12 +226,14 @@ Training <- R6Class("Training", } else { train_layer_res_list = lapply(X = 1:length(resampling), function (fold) { + if (verbose) { + cat(sprintf("Training for fold %s.\n", fold)) + } test_index = resampling[[fold]] train_index = setdiff(unlist(resampling), test_index) train_ids = self$getTargetValues()[train_index, 1L] self$trainLayer(ind_subset = train_ids, - use_var_sel = use_var_sel, - verbose = verbose) + use_var_sel = use_var_sel) test_ids = self$getTargetValues()[test_index, 1L] # TODO: Note: The current object is not a TestStudy, but a Training object. predicting = self$predictLayer(testing = self, @@ -272,8 +296,6 @@ Training <- R6Class("Training", #' Function for internal validation. If not specify, the \code{resampling} function from the package \code{caret} is used for a 10-folds cross-validation. #' @param resampling_arg (`list(1)`) \cr #' List of arguments to be passed to the function. - #' @param impute (`boolean`) \cr - #' If TRUE, mode or median based imputation is performed on the modality-specific predictions. #' @param verbose (`boolean`) \cr #' Warning messages will be displayed if set to TRUE. #' @@ -284,9 +306,7 @@ Training <- R6Class("Training", train = function (ind_subset = NULL, use_var_sel = FALSE, resampling_method = NULL, - resampling_arg = list(), - impute = TRUE, - verbose = TRUE) { + resampling_arg = list()) { # Test that the training object contains ovelapping individuals if (!self$testOverlap()) { stop("This Training object does not contain overlapping individuals.") #nocov @@ -300,12 +320,10 @@ Training <- R6Class("Training", self$createMetaTrainData(resampling_method, resampling_arg, use_var_sel = use_var_sel, - impute = impute, - verbose = verbose) + impute = private$impute) # 2) Train each layer self$trainLayer(ind_subset = ind_subset, - use_var_sel = use_var_sel, - verbose = verbose) + use_var_sel = use_var_sel) # 3) Train the meta layer # Add layer specific predictions to meta training layer layers = self$getKeyClass() @@ -421,7 +439,7 @@ Training <- R6Class("Training", for (k in layers$key) { layer = self$getFromHashTable(key = k) layer_var_sel = layer$varSelection(ind_subset = ind_subset, - verbose = verbose) + verbose = self$getVerbose()) if (length(layer_var_sel)) { # At least one variable selected selected = rbind(selected, @@ -587,6 +605,12 @@ Training <- R6Class("Training", return(private$target) }, #' @description + #' Getter of the verbose setting. + #' @export + getVerbose = function () { + return(private$verbose) + }, + #' @description #' Increase the number of trained layer. increaseNbTrainedLayer = function () { private$nb_trained_layer = private$nb_trained_layer + 1L @@ -611,6 +635,16 @@ Training <- R6Class("Training", return(private$target_obj) }, #' @description + #' Set imputation action na.action. + #' + #' @param impute `character(1)` \cr + #' How to handle missing values. + #' + setImpute = function(impute) { + private$impute = impute + invisible(self) + }, + #' @description #' Test that individuals overlap over layers. #' At least five individuals must overlapped. #' @@ -654,8 +688,8 @@ Training <- R6Class("Training", # This code accesses each layer (except TrainMetaLayer) level # and get the individual IDs. layers = layers[layers$class %in% "TrainLayer", ] - if (!nrow(layers)) { - stop("No available layer in this training object.") + if (!nrow(layers) | (nrow(layers) == 1L)) { + stop("No or only one available layer in this training object.") } ids_list = lapply(layers$key, function (k) { layer = self$getFromHashTable(key = k) @@ -696,6 +730,8 @@ Training <- R6Class("Training", target_obj = NULL, nb_trained_layer = 0L, impute = FALSE, + verbose = TRUE, + problem_typ = "classification", status = FALSE ), cloneable = FALSE diff --git a/R/VarSel.R b/R/VarSel.R index 6c8509a..9a0eeda 100644 --- a/R/VarSel.R +++ b/R/VarSel.R @@ -26,18 +26,31 @@ VarSel <- R6Class("VarSel", #' Layer on which the learner is stored. #' @param train_layer (`TrainLayer(1)`) \cr #' The training layer where to store the learner. + #' @param na_rm (`logical(1)`) \cr + #' If \code{TRUE}, the individuals with missing predictor values will be removed from the training dataset. initialize = function (id, package = NULL, varsel_fct, varsel_param, - train_layer) { + train_layer, + na_rm = TRUE) { private$id = id private$package = package private$varsel_fct = varsel_fct private$param = varsel_param + if (is.null(package)) { + if (!(exists(varsel_fct, envir = .GlobalEnv, inherits = TRUE) | is.function(get(varsel_fct, envir = .GlobalEnv)))) { + stop(sprintf("Function %s does not exists.\n Maybe you forget to specify its package?", varsel_fct)) + } + } if (!any(c("TrainLayer") %in% class(train_layer))) { stop("A variable selection tool can only belong to object of class TrainLayer.") } + if (!is.logical(na_rm)) { + stop("na.rm must be a logical value\n") + } else { + private$na_rm = na_rm + } # Remove VarSel if already existing if (train_layer$checkVarSelExist()) { key_class = train_layer$getKeyClass() @@ -137,8 +150,10 @@ VarSel <- R6Class("VarSel", train_data = private$train_layer$getTrainData() # Variable selection only on complete data train_data = train_data$clone(deep = FALSE) - complete_data = train_data$getCompleteData() - train_data$setDataFrame(data_frame = complete_data) + if (private$na_rm) { + complete_data = train_data$getCompleteData() + train_data$setDataFrame(data_frame = complete_data) + } if (is.null(private$package)) { varsel = private$varsel_fct } else { @@ -291,6 +306,7 @@ VarSel <- R6Class("VarSel", param = NULL, # Parameter interface to original names of arguments in original learning and predict function. param_interface = NULL, + na_rm = NULL, # Training layer (from class [TainLayer] or [TrainMetaLayer]) of the current learner. train_layer = NULL, # Individuals subset IDs. diff --git a/R/predict.bestSpecificLearner.R b/R/predict.bestSpecificLearner.R index 88919a6..9828811 100644 --- a/R/predict.bestSpecificLearner.R +++ b/R/predict.bestSpecificLearner.R @@ -16,7 +16,7 @@ #' Predicted target values are returned. #' #' @export -#' +#' @method predict bestSpecificLearner #' @importFrom stats weighted.mean complete.cases #' #' @examples diff --git a/R/predict.weightedMeanLearner.R b/R/predict.weightedMeanLearner.R index 71a787b..7eee769 100644 --- a/R/predict.weightedMeanLearner.R +++ b/R/predict.weightedMeanLearner.R @@ -16,6 +16,7 @@ #' Predicted target values are returned. #' #' @export +#' @method predict weightedMeanLearner #' #' @importFrom stats weighted.mean complete.cases #' diff --git a/R/testingFunctions.R b/R/testingFunctions.R index 3598a2a..620c323 100644 --- a/R/testingFunctions.R +++ b/R/testingFunctions.R @@ -6,14 +6,18 @@ #' Testing id. #' @param ind_col (`character(1)`) \cr #' Name of column of individuals IDs in testing data.frame. +#' @param verbose (`boolean`) \cr +#' Warning messages will be displayed if set to TRUE. #' @return #' A [Testing] object. #' @export createTesting = function (id, - ind_col) { + ind_col, + verbose = TRUE) { testing = Testing$new( id = id, - ind_col = ind_col + ind_col = ind_col, + verbose = verbose ) return(testing) } @@ -44,7 +48,11 @@ createTestLayer = function (testing, test_data = TestData$new(id = sprintf("%s_data", test_layer_id), data_frame = test_data, new_layer = test_layer) - return(testing) + if (testing$getVerbose()) { + return(testing) + } else { + invisible(TRUE) + } } #' @title Testing object Summaries @@ -57,7 +65,7 @@ createTestLayer = function (testing, #' Further arguments. #' #' @export -#' +#' @method summary Testing summary.Testing = function (object, ...) { return(object$summary()) } diff --git a/R/trainingFunctions.R b/R/trainingFunctions.R index 4902ca7..9a472b9 100644 --- a/R/trainingFunctions.R +++ b/R/trainingFunctions.R @@ -46,7 +46,7 @@ createTraining = function (id, #' @param varsel_package (`character(1)`) \cr #' Name of the package containing the function that implements the variable selection algorithm.\cr #' @param varsel_fct (`character(1)`) \cr -#' Name of the function that performs variable selection. +#' Name of the function that performs variable selection. For the default value NULL no variable selection will be performed. #' @param varsel_param (`list`) \cr #' List of arguments to be passed to \code{varsel_fct}. #' @param lrner_package (`character(1)`) \cr @@ -87,7 +87,7 @@ createTrainLayer = function (training, train_layer_id, train_data, varsel_package = NULL, - varsel_fct, + varsel_fct = NULL, varsel_param = list(), lrner_package, lrn_fct, @@ -108,18 +108,21 @@ createTrainLayer = function (training, data_frame = train_data, train_layer = train_layer) # Instantiate a VarSel object - var_sel = VarSel$new( - id = sprintf("%s_varsel", train_layer_id), - package = varsel_package, - varsel_fct = varsel_fct, - varsel_param = varsel_param, - train_layer - ) - var_sel$interface(x = x, - y = y, - object = object, - data = data, - extract_var_fct = extract_var_fct) + if (!is.null(varsel_fct)) { + var_sel = VarSel$new( + id = sprintf("%s_varsel", train_layer_id), + package = varsel_package, + varsel_fct = varsel_fct, + varsel_param = varsel_param, + train_layer = train_layer, + na_rm = na_rm + ) + var_sel$interface(x = x, + y = y, + object = object, + data = data, + extract_var_fct = extract_var_fct) + } # Instantiate a Lrner object lrner = Lrner$new( id = sprintf("%s_lrner", train_layer_id), @@ -135,7 +138,11 @@ createTrainLayer = function (training, object = object, data = data, extract_pred_fct = extract_pred_fct) - return(training) + if (training$getVerbose()) { + return(training) + } else { + invisible(TRUE) + } } @@ -156,8 +163,8 @@ createTrainLayer = function (training, #' List of arguments to be passed to \code{lrn_fct}. #' @param param_pred_list `(character(1)` \cr #' List of arguments to be passed to \code{predict} when computing predictions. -#' @param na_rm \cr -#' If \code{TRUE}, the individuals with missing predictor values will be removed from the training dataset. +#' @param na_action \cr +#' Handling of missing values in meta-data. Set to "na.keep" to keep missing values, "na.rm" to remove individuals with missing values or "na.impute" to impute missing values in meta-data. Only median and mode based imputations are actually handled. With the "na.keep" option, ensure that the provided meta-learner can handle missing values. #' @param x (`character(1)`) \cr #' If the name of the argument used by the provided original functions to pass #' the matrix of independent variable is not \code{x}, use this argument to specify how it is callled in the provided function. @@ -184,7 +191,7 @@ createTrainMetaLayer = function (training, lrn_fct, param_train_list = list(), param_pred_list = list(), - na_rm = TRUE, + na_action = "na.impute", x = "x", y = "y", object = "object", @@ -192,8 +199,24 @@ createTrainMetaLayer = function (training, extract_pred_fct = NULL) { # Instantiate a layer train_meta_layer = TrainMetaLayer$new(id = meta_layer_id, - training = training) + training = training) + impute = FALSE # Instantiate a Lrner object + if (na_action == "na.keep") { + na_rm = FALSE + } else { + if (na_action == "na.rm") { + na_rm = TRUE + } else { + if (na_action == "na.impute") { + na_rm = FALSE + impute = TRUE + } else { + stop("na_action must be one of 'na.fails', 'na.rm' or 'na.impute'.") + } + } + } + training$setImpute(impute = impute) meta_lrner = Lrner$new( id = sprintf("%s_lrner", meta_layer_id), package = lrner_package, @@ -201,7 +224,7 @@ createTrainMetaLayer = function (training, param_train_list = param_train_list, param_pred_list = param_pred_list, train_layer = train_meta_layer, - na_rm = TRUE + na_rm = na_rm ) meta_lrner$interface(x = x, y = y, @@ -219,17 +242,15 @@ createTrainMetaLayer = function (training, #' Training object where the created layer will be stored. #' @param ind_subset `vector(1)` \cr #' ID subset of individuals to be used for variable selection. -#' @param verbose (`boolean`) \cr -#' Warning messages will be displayed if set to TRUE. #' #' @return #' The current layer is returned with the resulting model. #' @export #' varSelection = function (training, - ind_subset = NULL, - verbose = TRUE) { - selected = training$varSelection(ind_subset = ind_subset, verbose) + ind_subset = NULL) { + selected = training$varSelection(ind_subset = ind_subset, + verbose = training$getVerbose()) return(selected) } @@ -247,10 +268,6 @@ varSelection = function (training, #' Function for internal validation. If not specify, the \code{resampling} function from the package \code{caret} is used for a 10-folds cross-validation. #' @param resampling_arg (`list(1)`) \cr #' List of arguments to be passed to the function. -#' @param verbose (`boolean`) \cr -#' Warning messages will be displayed if set to TRUE. -#' @param impute (`boolean`) \cr -#' If TRUE, either mode or median based imputation is performed for modality-specific predictions. #' #' @return #' The current object is returned, with each learner trained on each layer. @@ -260,15 +277,11 @@ fusemlr = function (training, ind_subset = NULL, use_var_sel = FALSE, resampling_method = NULL, - resampling_arg = list(), - impute = TRUE, - verbose = TRUE) { + resampling_arg = list()) { training$train(ind_subset = ind_subset, use_var_sel = use_var_sel, resampling_method = resampling_method, - resampling_arg = resampling_arg, - impute = impute, - verbose = verbose) + resampling_arg = resampling_arg) return(training) } @@ -288,11 +301,12 @@ fusemlr = function (training, #' @return #' The predicted object. All layers and the meta layer are predicted. This is the final predicted object. #' @export +#' @method predict Training predict.Training = function (object, testing, ind_subset = NULL) { predictions = object$predict(testing = testing, - ind_subset = ind_subset) + ind_subset = ind_subset) return(predictions) } @@ -356,21 +370,3 @@ upsetplot = function (object, ...) { object$upset(...) invisible(TRUE) } - -#' @title removeLayer -#' @description -#' Remove a [TrainLayer] or a [TrainMetaLayer] from the [Training] object passed as argument. -#' -#' @param training (`Training(1)`) \cr -#' Training object from which the layer will be removed. -#' @param layer_id (`character(1)`) \cr -#' ID of the layer to be removed. -#' @export -#' -removeLayer = function (training, layer_id = NULL) { - if (is.null(layer_id)) { - stop("Please provide the ID of the layer to be removed.") - } - training$removeLayer(id = layer_id) - return(training) -} diff --git a/R/weightedMeanLearner.R b/R/weightedMeanLearner.R index ba45595..352c860 100644 --- a/R/weightedMeanLearner.R +++ b/R/weightedMeanLearner.R @@ -14,7 +14,6 @@ #' A model object of class \code{weightedMeanLeaner}. #' #' @export -#' #' @examples #' set.seed(20240624L) #' x = data.frame(x1 = runif(n = 50L, min = 0, max = 1)) @@ -22,6 +21,9 @@ #' my_model = weightedMeanLearner(x = x, y = y) #' weightedMeanLearner = function (x, y, weighted = TRUE) { + if (!is.data.frame(x)) { + stop("x must be a data.frame.") + } # y must be binomial. 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zKuhn#67R?`(1yz)S(kwlB(e$I(4$EVtdx+!qe#|eK`OUMBVu379;JL>Gg}i1%^$3`WEuoP?x8212sL36;Js@}AFSWHgdqnROg-ARd>? zWIN)`3kI?Y#hbYd#z;gIiJ07mP#Z=@aU3mKVOTo*9+jk#hG>5Q6WG!kNT z30Wjch}euwEhN@r`XNa=jBG_hkj(wZ{xiWX#=ec19kS^jF0R=4O!h_Sg@Dc2i{pwB znqpK6Eb-ngBjbxmFD73lsLo|EIGgzNg29AIjv?8Jp$&2iBjdA35Ry`go+}CT5eH&a z0eq6;7(Orf9}HmVM?^9;gW(Is0?_?X%vagWaYzBn`^^UXty#YHe-3*3iNEP)s6@}n z++TVI2aahU>^YVDui>{U|7+m>+2c2y4EpbuKRW!*JiqVwUp+*BH2fx&|26#PAOAJ5 z|CgYDjv@S0a>1Wt{0^G`B>z*;-?sfd3Fps-KZp4DB(48{3hwitHMBJKHRm2Wqj^%B zy!%B=Fr2*h{kNCmzrE;Za_nB(s9xGQy|ndqMEDzc|Ib6q-){DQA13y8l1cS?dv6D! zzhjxv*jrC-Z?pfVm;dK!F|XJ2-rjEK^(K6;XUPBm$Hn~KX7BX~TF{$Fy`Gf!eq7v} zIfT9SB=JNV{_#Aqk1{U_+G}rreR}zKZ?C}}\preformatted{Training$trainLayer(ind_subset = NULL, use_var_sel = FALSE, verbose = TRUE)}\if{html}{\out{}} +\if{html}{\out{

}} } \subsection{Arguments}{ @@ -148,9 +150,6 @@ Subset of individuals IDs to be used for training.} \item{\code{use_var_sel}}{\code{boolean(1)} \cr If TRUE, selected variables available at each layer are used.} - -\item{\code{verbose}}{(\code{boolean}) \cr -Warning messages will be displayed if set to TRUE.} } \if{html}{\out{}} } @@ -192,8 +191,7 @@ Creates a meta training dataset and assigns it to the meta layer. resampling_method, resampling_arg, use_var_sel, - impute = TRUE, - verbose = TRUE + impute = TRUE )}\if{html}{\out{}} } @@ -211,9 +209,6 @@ If TRUE, selected variables available at each layer are used.} \item{\code{impute}}{(\code{boolean}) \cr If TRUE, mode or median based imputation is performed on the modality-specific predictions.} - -\item{\code{verbose}}{(\code{boolean}) \cr -Warning messages will be displayed if set to TRUE.} } \if{html}{\out{}} } @@ -231,9 +226,7 @@ Trains the current object. All leaners and the meta learner are trained. ind_subset = NULL, use_var_sel = FALSE, resampling_method = NULL, - resampling_arg = list(), - impute = TRUE, - verbose = TRUE + resampling_arg = list() )}\if{html}{\out{}} } @@ -252,9 +245,6 @@ Function for internal validation. If not specify, the \code{resampling} function \item{\code{resampling_arg}}{(\code{list(1)}) \cr List of arguments to be passed to the function.} -\item{\code{impute}}{(\code{boolean}) \cr -If TRUE, mode or median based imputation is performed on the modality-specific predictions.} - \item{\code{verbose}}{(\code{boolean}) \cr Warning messages will be displayed if set to TRUE.} } @@ -448,6 +438,16 @@ Getter of the target variable name. \if{html}{\out{
}}\preformatted{Training$getTarget()}\if{html}{\out{
}} } +} +\if{html}{\out{
}} +\if{html}{\out{
}} +\if{latex}{\out{\hypertarget{method-Training-getVerbose}{}}} +\subsection{Method \code{getVerbose()}}{ +Getter of the verbose setting. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{Training$getVerbose()}\if{html}{\out{
}} +} + } \if{html}{\out{
}} \if{html}{\out{}} @@ -481,6 +481,24 @@ Getter of the target object. \if{html}{\out{
}}\preformatted{Training$getTargetObj()}\if{html}{\out{
}} } +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-Training-setImpute}{}}} +\subsection{Method \code{setImpute()}}{ +Set imputation action na.action. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{Training$setImpute(impute)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{impute}}{\code{character(1)} \cr +How to handle missing values.} +} +\if{html}{\out{
}} +} } \if{html}{\out{
}} \if{html}{\out{}} diff --git a/man/VarSel.Rd b/man/VarSel.Rd index 6359000..b9dbfdd 100644 --- a/man/VarSel.Rd +++ b/man/VarSel.Rd @@ -31,7 +31,14 @@ Variable selection parameter list. Learner ID. \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{VarSel$new(id, package = NULL, varsel_fct, varsel_param, train_layer)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{VarSel$new( + id, + package = NULL, + varsel_fct, + varsel_param, + train_layer, + na_rm = TRUE +)}\if{html}{\out{
}} } \subsection{Arguments}{ @@ -53,6 +60,9 @@ Layer on which the learner is stored.} \item{\code{train_layer}}{(\code{TrainLayer(1)}) \cr The training layer where to store the learner.} + +\item{\code{na_rm}}{(\code{logical(1)}) \cr +If \code{TRUE}, the individuals with missing predictor values will be removed from the training dataset.} } \if{html}{\out{}} } diff --git a/man/createTesting.Rd b/man/createTesting.Rd index c3313fe..d45a170 100644 --- a/man/createTesting.Rd +++ b/man/createTesting.Rd @@ -4,7 +4,7 @@ \alias{createTesting} \title{createTesting} \usage{ -createTesting(id, ind_col) +createTesting(id, ind_col, verbose = TRUE) } \arguments{ \item{id}{(\code{character(1)}) \cr @@ -12,6 +12,9 @@ Testing id.} \item{ind_col}{(\code{character(1)}) \cr Name of column of individuals IDs in testing data.frame.} + +\item{verbose}{(\code{boolean}) \cr +Warning messages will be displayed if set to TRUE.} } \value{ A \link{Testing} object. diff --git a/man/createTrainLayer.Rd b/man/createTrainLayer.Rd index eb48a8e..21607d5 100644 --- a/man/createTrainLayer.Rd +++ b/man/createTrainLayer.Rd @@ -9,7 +9,7 @@ createTrainLayer( train_layer_id, train_data, varsel_package = NULL, - varsel_fct, + varsel_fct = NULL, varsel_param = list(), lrner_package, lrn_fct, @@ -38,7 +38,7 @@ Data modality to be stored in \link{TrainData}.} Name of the package containing the function that implements the variable selection algorithm.\cr} \item{varsel_fct}{(\code{character(1)}) \cr -Name of the function that performs variable selection.} +Name of the function that performs variable selection. For the default value NULL no variable selection will be performed.} \item{varsel_param}{(\code{list}) \cr List of arguments to be passed to \code{varsel_fct}.} diff --git a/man/createTrainMetaLayer.Rd b/man/createTrainMetaLayer.Rd index c62504e..b2bd331 100644 --- a/man/createTrainMetaLayer.Rd +++ b/man/createTrainMetaLayer.Rd @@ -11,7 +11,7 @@ createTrainMetaLayer( lrn_fct, param_train_list = list(), param_pred_list = list(), - na_rm = TRUE, + na_action = "na.impute", x = "x", y = "y", object = "object", @@ -38,8 +38,8 @@ List of arguments to be passed to \code{lrn_fct}.} \item{param_pred_list}{\verb{(character(1)} \cr List of arguments to be passed to \code{predict} when computing predictions.} -\item{na_rm}{\cr -If \code{TRUE}, the individuals with missing predictor values will be removed from the training dataset.} +\item{na_action}{\cr +Handling of missing values in meta-data. Set to "na.keep" to keep missing values, "na.rm" to remove individuals with missing values or "na.impute" to impute missing values in meta-data. Only median and mode based imputations are actually handled. With the "na.keep" option, ensure that the provided meta-learner can handle missing values.} \item{x}{(\code{character(1)}) \cr If the name of the argument used by the provided original functions to pass diff --git a/man/fusemlr.Rd b/man/fusemlr.Rd index 0b4b6d2..c6af1f0 100644 --- a/man/fusemlr.Rd +++ b/man/fusemlr.Rd @@ -9,9 +9,7 @@ fusemlr( ind_subset = NULL, use_var_sel = FALSE, resampling_method = NULL, - resampling_arg = list(), - impute = TRUE, - verbose = TRUE + resampling_arg = list() ) } \arguments{ @@ -29,12 +27,6 @@ Function for internal validation. If not specify, the \code{resampling} function \item{resampling_arg}{(\code{list(1)}) \cr List of arguments to be passed to the function.} - -\item{impute}{(\code{boolean}) \cr -If TRUE, either mode or median based imputation is performed for modality-specific predictions.} - -\item{verbose}{(\code{boolean}) \cr -Warning messages will be displayed if set to TRUE.} } \value{ The current object is returned, with each learner trained on each layer. diff --git a/man/multi_omics.Rd b/man/multi_omics.Rd new file mode 100644 index 0000000..3d22bec --- /dev/null +++ b/man/multi_omics.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/multi_omics.R +\docType{data} +\name{multi_omics} +\alias{multi_omics} +\title{Simulated multiomics data for 70 training participants and 23 testing participants, +each with an effect size of 20 on each layer. Each layer includes 50 participants for +training and 20 for testing. Participants do not perfectly overlap across layers. +The simulation is based on the R package \code{interSIM}.} +\format{ +A list with training and testing data contaning methylation, gene expressions and protein expressions data. +} +\usage{ +data(multi_omics) +} +\description{ +The dataset is a list containing training and testing data, +called \code{training} and \code{testing} respectively. Each data is a list +containing the following multi_omics at each layer. +} +\details{ +\itemize{ +\item \code{methylation}: A \code{data.frame} containing the simulated methylation dataset. +\item \code{genexpr} : A \code{data.frame} containing the gene expression dataset. +\item \code{proteinexpr}: A \code{data.frame} containing the protein expression dataset. +\item \code{target}: A \code{data.frame} with two columns, containing patient IDs and values of target variable. +} +} +\keyword{datasets} diff --git a/man/varSelection.Rd b/man/varSelection.Rd index 92e64f4..e88f124 100644 --- a/man/varSelection.Rd +++ b/man/varSelection.Rd @@ -4,7 +4,7 @@ \alias{varSelection} \title{varSelection} \usage{ -varSelection(training, ind_subset = NULL, verbose = TRUE) +varSelection(training, ind_subset = NULL) } \arguments{ \item{training}{(\code{Training(1)}) \cr @@ -12,9 +12,6 @@ Training object where the created layer will be stored.} \item{ind_subset}{\code{vector(1)} \cr ID subset of individuals to be used for variable selection.} - -\item{verbose}{(\code{boolean}) \cr -Warning messages will be displayed if set to TRUE.} } \value{ The current layer is returned with the resulting model. diff --git a/tests/testthat/test-Data.R b/tests/testthat/test-Data.R index f7ddb27..98ccd50 100644 --- a/tests/testthat/test-Data.R +++ b/tests/testthat/test-Data.R @@ -1,7 +1,7 @@ -data("entities") +data("multi_omics") test_data <- Data$new(id = "geneexpr", ind_col = "IDS", - data_frame = entities$training$geneexpr[-11L, ]) + data_frame = multi_omics$training$geneexpr[-11L, ]) test_that("Data: initialize is correct", { expect_true(R6::is.R6(test_data)) diff --git a/tests/testthat/test-Lrner.R b/tests/testthat/test-Lrner.R index f4308fd..f02bd3a 100644 --- a/tests/testthat/test-Lrner.R +++ b/tests/testthat/test-Lrner.R @@ -2,7 +2,7 @@ training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) # Training layers tl_geneexpr <- TrainLayer$new(id = "geneexpr", training = training) diff --git a/tests/testthat/test-Model.R b/tests/testthat/test-Model.R index 5d3ee00..9cdb46d 100644 --- a/tests/testthat/test-Model.R +++ b/tests/testthat/test-Model.R @@ -1,10 +1,10 @@ -data("entities") +data("multi_omics") test_that("Model: all tests", { # Prepare training object and layers training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) tl_ge <- TrainLayer$new(id = "geneexpr", training = training) lrner_ge <- Lrner$new(id = "ranger", package = "ranger", @@ -15,7 +15,7 @@ test_that("Model: all tests", { train_layer = tl_ge) train_data_ge <- TrainData$new(id = "geneexpr", train_layer = tl_ge, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) expect_no_error({ tl_ge$train() }) @@ -31,7 +31,7 @@ test_that("Model: all tests", { nl_ge <- TestLayer$new(id = "wrong_id", testing = testing) testing_data_ge <- TestData$new(id = "geneexpr", new_layer = nl_ge, - data_frame = entities$testing$geneexpr) + data_frame = multi_omics$testing$geneexpr) expect_error({ my_model$predict(testing_data_ge) }) diff --git a/tests/testthat/test-TestData.R b/tests/testthat/test-TestData.R index 7e17840..2f3e758 100644 --- a/tests/testthat/test-TestData.R +++ b/tests/testthat/test-TestData.R @@ -1,4 +1,4 @@ -data("entities") +data("multi_omics") test_that("TestData: complement tests", { # Testing and layers with wrong ID column testing_wrong_id <- Testing$new(id = "testing", ind_col = "IDSS") @@ -6,7 +6,7 @@ test_that("TestData: complement tests", { expect_error({ testing_data_ge <- TestData$new(id = "geneexpr", new_layer = nl_ge, - data_frame = entities$testing$geneexpr) + data_frame = multi_omics$testing$geneexpr) }) # Prepare testing and layers with correct ID column testing <- Testing$new(id = "testing", ind_col = "IDS") @@ -14,15 +14,15 @@ test_that("TestData: complement tests", { expect_error({ testing_data_ge <- TestData$new(id = "geneexpr", new_layer = "layer", - data_frame = entities$testing$geneexpr) + data_frame = multi_omics$testing$geneexpr) }) expect_no_error({ testing_data_ge <- TestData$new(id = "geneexpr", new_layer = nl_ge, - data_frame = entities$testing$geneexpr) + data_frame = multi_omics$testing$geneexpr) testing_data_ge <- TestData$new(id = "geneexpr", new_layer = nl_ge, - data_frame = entities$testing$geneexpr) + data_frame = multi_omics$testing$geneexpr) }) }) diff --git a/tests/testthat/test-TrainData.R b/tests/testthat/test-TrainData.R index a46d692..c9730a4 100644 --- a/tests/testthat/test-TrainData.R +++ b/tests/testthat/test-TrainData.R @@ -1,17 +1,17 @@ -data("entities") +data("multi_omics") test_that("TrainData: all tests", { # Testing and layers training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # # Start testing training data with empty layer # # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # expect_error({ train_data_ge <- TrainData$new(id = "geneexpr", train_layer = training, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) }) tl_ge <- TrainLayer$new(id = "geneexpr", training = training) tl_pr <- TrainLayer$new(id = "proteinexpr", training = training) @@ -26,13 +26,13 @@ test_that("TrainData: all tests", { expect_no_error({ train_data_ge <- TrainData$new(id = "geneexpr", train_layer = tl_ge, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) train_data_pr <- TrainData$new(id = "proteinexpr", train_layer = tl_pr, - data_frame = entities$training$proteinexpr) + data_frame = multi_omics$training$proteinexpr) train_data_me <- TrainData$new(id = "methylation", train_layer = tl_me, - data_frame = entities$training$methylation) + data_frame = multi_omics$training$methylation) train_data_me$getTrainLayer() print(train_data_me) }) @@ -42,7 +42,7 @@ test_that("TrainData: all tests", { ts_wrong_target <- Training$new(id = "training", ind_col = "IDS", target = "diseases", - target_df = entities$training$target) + target_df = multi_omics$training$target) }) expect_error({ tl_ge_wrong_target <- TrainLayer$new(id = "geneexpr", @@ -51,18 +51,18 @@ test_that("TrainData: all tests", { expect_error({ TrainData$new(id = "geneexpr", train_layer = tl_ge_wrong_target, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) }) # No target variable ts_missing_target <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) tl_ge_missing_target <- TrainLayer$new(id = "geneexpr", training = ts_missing_target) expect_no_error({ - tmp <- entities$training$geneexpr + tmp <- multi_omics$training$geneexpr # Re-build this test case # tmp$disease[1] <- NA TrainData$new(id = "geneexpr", @@ -86,10 +86,10 @@ test_that("TrainData: all tests", { ts_not_bin_target <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) tl_ge_not_bin_target <- TrainLayer$new(id = "geneexpr_no_bin", training = ts_not_bin_target) - tmp <- entities$training$geneexpr + tmp <- multi_omics$training$geneexpr tmp$disease <- sample(x = letters[1L:3L], size = length(tmp$disease), replace = TRUE) diff --git a/tests/testthat/test-TrainLayer.R b/tests/testthat/test-TrainLayer.R index 3a0046a..7350b61 100644 --- a/tests/testthat/test-TrainLayer.R +++ b/tests/testthat/test-TrainLayer.R @@ -1,10 +1,10 @@ -data("entities") +data("multi_omics") test_that("TrainLayer: all tests", { # Training and layers training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) # A TrainLayer can only belong to a Training expect_error({ TrainLayer$new(id = "geneexpr", training = "not_a_training") @@ -51,7 +51,7 @@ test_that("TrainLayer: all tests", { expect_error({tl_ge$getTestData()}) train_data_ge <- TrainData$new(id = "geneexpr", train_layer = tl_ge, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) testing <- Testing$new(id = "testing", ind_col = "IDS") nl_ge <- TestLayer$new(id = "geneexpr", testing = testing) @@ -80,7 +80,7 @@ test_that("TrainLayer: all tests", { }) testing_data_ge <- TestData$new(id = "geneexpr", new_layer = nl_ge, - data_frame = entities$testing$geneexpr) + data_frame = multi_omics$testing$geneexpr) expect_no_error({ tl_ge$predict(nl_ge) }) diff --git a/tests/testthat/test-TrainMetaLayer.R b/tests/testthat/test-TrainMetaLayer.R index 36e01f2..60bfadc 100644 --- a/tests/testthat/test-TrainMetaLayer.R +++ b/tests/testthat/test-TrainMetaLayer.R @@ -1,10 +1,10 @@ -data("entities") +data("multi_omics") test_that("TrainLayer: all tests", { # Training and layers training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) tl_meta <- TrainMetaLayer$new(id = "meta", training = training) # A TrainLayer can only belong to a Training @@ -41,7 +41,7 @@ test_that("TrainLayer: all tests", { train_layer = tl_ge) train_data_ge <- TrainData$new(id = "geneexpr", train_layer = tl_ge, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) expect_error({ tl_meta$train() }) diff --git a/tests/testthat/test-Training.R b/tests/testthat/test-Training.R index 6a37430..b3128e5 100644 --- a/tests/testthat/test-Training.R +++ b/tests/testthat/test-Training.R @@ -1,4 +1,4 @@ -data("entities") +data("multi_omics") set.seed(5214) test_that("Training: all tests", { # Create training @@ -6,26 +6,26 @@ test_that("Training: all tests", { training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) print(training) }) expect_error({ Training$new(id = 1, ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) }) expect_error({ Training$new(id = "training", ind_col = 1, target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) }) expect_error({ Training$new(id = "training", ind_col = "IDS", target = 1, - target_df = entities$training$target) + target_df = multi_omics$training$target) }) expect_error({ Training$new(id = "training", @@ -46,11 +46,11 @@ test_that("Training: all tests", { training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target, + target_df = multi_omics$training$target, problem_type = "test") }) expect_warning({ - tmp <- entities$training$target + tmp <- multi_omics$training$target tmp$disease <- sample(x = 1:3, size = nrow(tmp), replace = TRUE) @@ -64,7 +64,7 @@ test_that("Training: all tests", { training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target, + target_df = multi_omics$training$target, problem_type = "regression") }) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ @@ -125,10 +125,10 @@ test_that("Training: all tests", { expect_no_error({ train_data_ge <- TrainData$new(id = "geneexpr", train_layer = tl_ge, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) train_data_pr <- TrainData$new(id = "proteinexpr", train_layer = tl_pr, - data_frame = entities$training$proteinexpr) + data_frame = multi_omics$training$proteinexpr) }) # Upset plot works expect_no_error({ @@ -250,10 +250,10 @@ test_that("Training: all tests", { expect_no_error({ testing_data_ge <- TestData$new(id = "geneexpr", new_layer = nl_ge, - data_frame = entities$testing$geneexpr) + data_frame = multi_omics$testing$geneexpr) testing_data_pr <- TestData$new(id = "proteinexpr", new_layer = nl_pr, - data_frame = entities$testing$proteinexpr) + data_frame = multi_omics$testing$proteinexpr) testing$summary() testing$upset() }) diff --git a/tests/testthat/test-VarSel.R b/tests/testthat/test-VarSel.R index 1ec1ca7..bcf394f 100644 --- a/tests/testthat/test-VarSel.R +++ b/tests/testthat/test-VarSel.R @@ -2,11 +2,11 @@ test_that("VarSel: with package for variable selection", { training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) tl_ge <- TrainLayer$new(id = "geneexpr", training = training) train_data_ge <- TrainData$new(id = "geneexpr", train_layer = tl_ge, - data_frame = entities$training$geneexpr) + data_frame = multi_omics$training$geneexpr) expect_error({ var_sel <- VarSel$new(id = "varsel_geneexpr", package = "Boruta", @@ -40,7 +40,7 @@ test_that("VarSel: with package for variable selection", { train_layer = tl_ge) }) expect_no_error({ - varsel_ge$varSelection(ind_subset = entities$training$geneexpr$IDS[1:45]) + varsel_ge$varSelection(ind_subset = multi_omics$training$geneexpr$IDS[1:45]) varsel_ge$getId() }) }) @@ -49,18 +49,19 @@ test_that("VarSel: with function for variable selection", { training <- Training$new(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target) + target_df = multi_omics$training$target) tl_ge <- TrainLayer$new(id = "geneexpr", training = training) train_data_ge <- TrainData$new(id = "geneexpr", train_layer = tl_ge, - data_frame = entities$training$geneexpr) - var_sel <- VarSel$new(id = "varsel_geneexpr", - varsel_fct = "test_var_sel", - varsel_param = list(), - train_layer = tl_ge) + data_frame = multi_omics$training$geneexpr) + expect_error({ #TODO: check why it only works in interactiv mode - test_var_sel = function (x, y) {return(matrix(1:4, 2L, 2L))} + test_var_sel <- function (x, y) {return(matrix(1:4, 2L, 2L))} + var_sel <- VarSel$new(id = "varsel_geneexpr", + varsel_fct = "test_var_sel", + varsel_param = list(), + train_layer = tl_ge) tl_ge$varSelection() }) }) diff --git a/tests/testthat/test-bestSpecificLearner.R b/tests/testthat/test-bestSpecificLearner.R index e179ec6..5005d27 100644 --- a/tests/testthat/test-bestSpecificLearner.R +++ b/tests/testthat/test-bestSpecificLearner.R @@ -15,7 +15,7 @@ test_that("bestSpecificLearner works", { x_new <- data.frame(x2 = rnorm(10)) my_predictions <- predict(object = my_model, data = x_new) }) - expect_error({ + expect_no_error({ set.seed(20240624L) x = data.frame(x1 = runif(n = 50L, min = 0, max = 1)) y = sample(x = 0L:2L, size = 50L, replace = TRUE) diff --git a/tests/testthat/test-mutli_omics.R b/tests/testthat/test-mutli_omics.R new file mode 100644 index 0000000..218ea07 --- /dev/null +++ b/tests/testthat/test-mutli_omics.R @@ -0,0 +1,3 @@ +test_that("multi_omics works", { + expect_no_error(data("multi_omics")) +}) diff --git a/tests/testthat/test-weightedMeanLearner.R b/tests/testthat/test-weightedMeanLearner.R index c379154..dd32b4f 100644 --- a/tests/testthat/test-weightedMeanLearner.R +++ b/tests/testthat/test-weightedMeanLearner.R @@ -23,7 +23,7 @@ test_that("weightedMeanLearner works", { x_new <- data.frame(x2 = rnorm(10)) my_predictions <- predict(object = my_model, data = x_new) }) - expect_error({ + expect_no_error({ set.seed(20240624L) x = data.frame(x1 = rnorm(50L)) y = sample(x = 0L:2L, size = 50L, replace = TRUE) diff --git a/vignettes/how_to_use.Rmd b/vignettes/how_to_use.Rmd index a494b1e..f1c88ac 100644 --- a/vignettes/how_to_use.Rmd +++ b/vignettes/how_to_use.Rmd @@ -31,10 +31,10 @@ library(fuseMLR) The following example is based on simulated data available in `fuseMLR`. Data have been simulated using the R package `InterSIM`, version 2.2.0. Two types of data were simulated: training and testing datasets. Each consists of four `data.frame`s—gene expression, protein expression, methylation data, and target variable observations. Individuals are organized in rows, variables in columns, with an additional column for individual IDs. In total, $70$ individuals with $50$ individuals pro layer have been simulated for training, and $23$ ($20$ per layer) for testing. Individuals do not necessarily overlapped. Effects have been introduced for gene expression and methylation by shifting the means by $0.5$ to create case-control study with $50$% prevalence. Individuals do not necessarily overlap. Effects were introduced in gene expression and methylation by shifting the means by 0.5 to create a case-control study. For illustration, the number of variables was kept smaller than what is typically expected in reality. The data simulation code is available [here](https://github.com/imbs-hl/fuseMLR/blob/master/test_code/build_data.R). ```{r data_exam, include=TRUE, eval=TRUE} -data("entities") +data("multi_omics") # This is a list containing two lists of data: training and test. -# Each sublist contains three entities. -str(object = entities, max.level = 2L) +# Each sublist contains three omics data. +str(object = multi_omics, max.level = 2L) ``` ## C - Training # @@ -45,8 +45,8 @@ The training process is handled in **fuseMLR** by a class called **Training**. W training <- createTraining(id = "training", ind_col = "IDS", target = "disease", - target_df = entities$training$target, - verbose = FALSE) + target_df = multi_omics$training$target, + verbose = TRUE) print(training) ``` @@ -64,48 +64,54 @@ The following code adds a gene expression, a protein abundance, and a methylatio # Create gene expression layer createTrainLayer(training = training, train_layer_id = "geneexpr", - train_data = entities$training$geneexpr, + train_data = multi_omics$training$geneexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), na_rm = TRUE) # Create gene protein abundance layer createTrainLayer(training = training, train_layer_id = "proteinexpr", - train_data = entities$training$proteinexpr, + train_data = multi_omics$training$proteinexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(type = "response"), na_rm = TRUE) # Create methylation layer createTrainLayer(training = training, train_layer_id = "methylation", - train_data = entities$training$proteinexpr, + train_data = multi_omics$training$methylation, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, mtry = 3L, - probability = TRUE), + probability = TRUE, + na.action = "na.learn"), lrner_package = "ranger", lrn_fct = "ranger", param_train_list = list(probability = TRUE, - mtry = 1L), + mtry = 1L, + na.action = "na.learn"), param_pred_list = list(), na_rm = TRUE) ``` @@ -120,7 +126,7 @@ createTrainMetaLayer(training = training, lrn_fct = "weightedMeanLearner", param_train_list = list(), param_pred_list = list(), - na_rm = FALSE) + na_action = "na.impute") ``` Function ```upsetplot()``` is available to generate an upset of the training data, i.e. an overview how patients overlap across layers. @@ -136,8 +142,7 @@ Function ```varSelection()``` performs modality-specific variable selection. ```{r varsel, include=TRUE, eval=TRUE} # Variable selection set.seed(5467) -var_sel_res <- varSelection(training = training, - verbose = FALSE) +var_sel_res <- varSelection(training = training) print(var_sel_res) ``` @@ -147,12 +152,10 @@ For each layer, the variable selection results show the chosen variables. In thi We use the function ```fusemlr()``` to train our models using the subset of selected variables. -```{r lrner_train, include=TRUE, eval=TRUE} +```{r lrner_train, include=TRUE, eval=TRUE, message=TRUE} set.seed(5462) fusemlr(training = training, - use_var_sel = TRUE, - impute = TRUE, - verbose = FALSE) + use_var_sel = TRUE) print(training) # See also summary(training) @@ -178,17 +181,17 @@ testing <- createTesting(id = "testing", # Create gene expression layer createTestLayer(testing = testing, test_layer_id = "geneexpr", - test_data = entities$testing$geneexpr) + test_data = multi_omics$testing$geneexpr) # Create gene protein abundance layer createTestLayer(testing = testing, test_layer_id = "proteinexpr", - test_data = entities$testing$proteinexpr) + test_data = multi_omics$testing$proteinexpr) # Create methylation layer createTestLayer(testing = testing, test_layer_id = "methylation", - test_data = entities$testing$proteinexpr) + test_data = multi_omics$testing$methylation) ``` We can also generate an upset plot to visualize patient overlap across testing layers. @@ -209,7 +212,7 @@ print(predictions) ```{r performance_all, include=TRUE, eval=TRUE} pred_values <- predictions$predicted_values actual_pred <- merge(x = pred_values, - y = entities$testing$target, + y = multi_omics$testing$target, by = "IDS", all.y = TRUE) x <- as.integer(actual_pred$disease == 2L) @@ -246,12 +249,10 @@ We distinguish common supervised learning arguments from method specific argumen The interface approach leverages the arguments in ```createTrainLayer()``` to map the argument names of the original learning function. In the example below, the gene expression layer is re-created using the ```svm``` (Support Vector Machine) function from the ```e1071``` package as the learner. A discrepancy arises in the argument names of the ```predict.svm``` function, which uses ```object``` and ```newdata```. ```{r interface, include=TRUE, eval=FALSE} -# Remove the current gene expression layer from training -removeLayer(training = training, layer_id = "geneexpr") # Re-create the gene expression layer with support vector machine as learner. createTrainLayer(training = training, train_layer_id = "geneexpr", - train_data = entities$training$geneexpr, + train_data = multi_omics$training$geneexpr, varsel_package = "Boruta", varsel_fct = "Boruta", varsel_param = list(num.trees = 1000L, @@ -275,12 +276,10 @@ createTrainLayer(training = training, ) # Variable selection set.seed(5467) -var_sel_res <- varSelection(training = training, - verbose = FALSE) +var_sel_res <- varSelection(training = training) set.seed(5462) training <- fusemlr(training = training, - use_var_sel = TRUE, - verbose = FALSE) + use_var_sel = TRUE) print(training) ``` @@ -326,11 +325,10 @@ createTrainMetaLayer(training = training, lrner_package = NULL, lrn_fct = "mylasso", param_train_list = list(nlambda = 100L), - na_rm = TRUE) + na_action = "na.impute") set.seed(5462) training <- fusemlr(training = training, - use_var_sel = TRUE, - verbose = FALSE) + use_var_sel = TRUE) print(training) ```