From 7051ecd47ce61d40749fd3f5210943774f17a877 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 8 Aug 2023 15:46:06 +0200 Subject: [PATCH 01/28] add manual CV for IHT --- R/CyclopsModels.R | 152 +++++++++++++++++++++++++++++++++++++++++--- R/CyclopsSettings.R | 20 ++++-- 2 files changed, 159 insertions(+), 13 deletions(-) diff --git a/R/CyclopsModels.R b/R/CyclopsModels.R index d9b3ee0ec..74816e259 100644 --- a/R/CyclopsModels.R +++ b/R/CyclopsModels.R @@ -39,20 +39,20 @@ fitCyclopsModel <- function( time = .data$survivalTime ) - covariates <- filterCovariateIds(param, trainData$covariateData) + trainData$covariateData$covariates <- filterCovariateIds(param, trainData$covariateData) if (!is.null(param$priorCoefs)) { sourceCoefs <- param$priorCoefs %>% dplyr::filter(abs(.data$betas)>0 & .data$covariateIds != "(Intercept)") - newCovariates <- covariates %>% + newCovariates <- trainData$covariateData$covariates %>% dplyr::filter(.data$covariateId %in% !!sourceCoefs$covariateIds) %>% dplyr::mutate(newCovariateId = .data$covariateId*-1) %>% dplyr::select(-"covariateId") %>% dplyr::rename(covariateId = .data$newCovariateId) %>% dplyr::collect() - Andromeda::appendToTable(covariates, newCovariates) + Andromeda::appendToTable(trainData$covariateData$covariates, newCovariates) } @@ -60,7 +60,7 @@ fitCyclopsModel <- function( cyclopsData <- Cyclops::convertToCyclopsData( outcomes = trainData$covariateData$labels, - covariates = covariates, + covariates = trainData$covariateData$covariates, addIntercept = settings$addIntercept, modelType = modelTypeToCyclopsModelType(settings$modelType), checkRowIds = FALSE, @@ -97,7 +97,8 @@ fitCyclopsModel <- function( ParallelLogger::logInfo("Determining initialRidgeVariance") Cyclops::fitCyclopsModel(cyclopsData, prior = normalPrior, - control = normalControl)}, + control = normalControl, + warnings = FALSE)}, finally = ParallelLogger::logInfo("Done.")) param$priorParams$initialRidgeVariance <- fit$variance } @@ -132,9 +133,17 @@ fitCyclopsModel <- function( )}, finally = ParallelLogger::logInfo('Done.') ) + } else if (settings$manualCV) { + result <- doCyclopsCVPenalty(data = trainData, + prior = prior, + fixedCoefficients = fixedCoefficients, + startingCoefficients = startingCoefficients, + nTries = settings$nTries) + fit <- result$modelFit + hyperParamSearch <- result$hyperParamSearch } else{ fit <- tryCatch({ - ParallelLogger::logInfo('Running Cyclops with fixed varience') + ParallelLogger::logInfo('Running Cyclops with fixed variance') Cyclops::fitCyclopsModel(cyclopsData, prior = prior)}, finally = ParallelLogger::logInfo('Done.')) } @@ -173,7 +182,7 @@ fitCyclopsModel <- function( prediction$evaluationType <- 'Train' # get cv AUC if exists - cvPerFold <- data.frame() + hyperParamSearch <- data.frame() if(!is.null(modelTrained$cv)){ cvPrediction <- do.call(rbind, lapply(modelTrained$cv, function(x){x$predCV})) cvPrediction$evaluationType <- 'CV' @@ -199,6 +208,7 @@ fitCyclopsModel <- function( # remove the cv from the model: modelTrained$cv <- NULL + hyperParamSearch <- cvPerFold } result <- list( @@ -237,7 +247,7 @@ fitCyclopsModel <- function( variance = modelTrained$priorVariance, log_likelihood = modelTrained$log_likelihood ), - hyperParamSearch = cvPerFold + hyperParamSearch = hyperParamSearch ), covariateImportance = variableImportance @@ -542,4 +552,130 @@ reparamTransferCoefs <- function(inCoefs) { coefs <- data.frame(betas = coefs, covariateIds = rownames(coefs), row.names = NULL) return(coefs) +} + +#' @details do simple CV to determine best penalty manually +#' +#' Will try penalty `BIC` down to `penaltyRatio` * `BIC`. How many penalties +#' to try is determined by `nTries.` +#' +#' @param data The training data +#' @param prior Cyclops prior to use +#' @param fixedCoefficients What coefficients (if any) should be fixed +#' @param startingCoefficients What coefficients (if any) should have some starting value +#' @param penaltyRatio This controls the lowest penalty to try as a ratio of `BIC` penalty. +#' Trying very low penalties will increase computation times a lot. +#' @param nTries How many penalties to try. Default: 10 +doCyclopsCVPenalty <- function(data, + prior, + fixedCoefficients, + startingCoefficients, + penaltyRatio = 0.1, + nTries = 10) { + + # first penalty to try is the strict BIC + startingPenalty <- log(nrow(data$labels)) / 2 + + penalties <- seq(from = startingPenalty, + to = penaltyRatio * startingPenalty, + length.out = nTries) + + nFolds <- length(unique(data$folds$index)) + hyperParamSearch <- data.frame(matrix(nrow=length(penalties), ncol=nFolds+2)) + colnames(hyperParamSearch) <- c("Penalty", "avg_CV", unlist(lapply(seq_len(nFolds), + function(x) paste0("Fold_", x)))) + + ParallelLogger::logInfo("Performing hyperparameter tuning to determine best penalty") + + start <- Sys.time() + + data$covariateData$folds <- data$folds # folds needs to be Andromeda to for joins to work + on.exit(data$covariateData$folds <- NULL) + + data$covariateData$covariates <- data$covariateData$covariates %>% + dplyr::inner_join(data$covariateData$folds, by='rowId') %>% dplyr::collect() + + data$covariateData$labels <- data$covariateData$labels %>% + dplyr::inner_join(data$covariateData$folds, by='rowId') %>% dplyr::collect() + + for (i in seq_along(penalties)) { + hyperParamSearch[i, "Penalty"] <- penalties[i] + prior$penalty <- penalties[i] + + for (fold in seq_len(nFolds)) { + + trainData <- data$covariateData$covariates %>% + dplyr::filter(index!=fold) %>% + dplyr::select(c("rowId", "covariateId", "covariateValue")) %>% + dplyr::collect() + + trainOutcomes <- data$covariateData$labels %>% + dplyr::filter(index!=fold) %>% + dplyr::select(-index) %>% + dplyr::collect() + + cyclopsData <- Cyclops::convertToCyclopsData(outcomes = trainOutcomes, + covariates = trainData, + modelType = 'lr', + addIntercept = TRUE, + checkRowIds = FALSE, + normalize = NULL, + quiet=TRUE) + + fit <- Cyclops::fitCyclopsModel( + cyclopsData = cyclopsData, + prior = prior, + fixedCoefficients = fixedCoefficients, + startingCoefficients = startingCoefficients, + warnings = FALSE) + + + # predict on held out fold + testData <- data$covariateData$covariates %>% + dplyr::filter(index==fold) %>% + dplyr::select(c("rowId", "covariateId", "covariateValue")) %>% + dplyr::mutate(covariateId=as.numeric(covariateId)) %>% + dplyr::collect() + + testOutcomes <- data$covariateData$labels %>% + dplyr::filter(index==fold) %>% + dplyr::select(-index) %>% + dplyr::collect() + + preds <- predict(fit, newCovariates = testData, + newOutcomes = testOutcomes) + + # calculate performance + prediction <- data.frame(outcomeCount=testOutcomes$outcomeCount, + value=preds) + attr(prediction, "metaData")$modelType <- "binary" + auc <- computeAuc(prediction) + hyperParamSearch[i, paste0("Fold_", fold)] <- auc + } + hyperParamSearch[i, "avg_CV"] <- mean(as.numeric(hyperParamSearch[i, seq(3,3 + nFolds - 1)])) + } + + bestIndex <- which.max(hyperParamSearch$avg_CV) + bestPenalty <- hyperParamSearch[bestIndex, "Penalty"] + delta <- Sys.time() - start + ParallelLogger::logInfo(paste0("HyperParameter tuning took ", signif(delta, 3), " ", attr(delta, "units"))) + ParallelLogger::logInfo(paste0("Best penalty value is: ", signif(bestPenalty, 4), + " With performance of: ", signif(hyperParamSearch[bestIndex, "avg_CV"]))) + # refit at best value + + prior$penalty <- bestPenalty + cyclopsData <- Cyclops::convertToCyclopsData(data$covariateData$labels, + data$covariateData$covariates, + checkRowIds = FALSE, + quiet=TRUE) + ParallelLogger::logInfo("Refitting model with best penalty on training set") + fit <- Cyclops::fitCyclopsModel( + cyclopsData = cyclopsData, + prior = prior, + fixedCoefficients = fixedCoefficients, + startingCoefficients = startingCoefficients + ) + + results <- list(modelFit = fit, + hyperParamSearch = hyperParamSearch) } \ No newline at end of file diff --git a/R/CyclopsSettings.R b/R/CyclopsSettings.R index 656fb0706..6a7f62dd0 100644 --- a/R/CyclopsSettings.R +++ b/R/CyclopsSettings.R @@ -169,16 +169,18 @@ setCoxModel <- function( #' #' @param K The maximum number of non-zero predictors #' @param penalty Specifies the IHT penalty; possible values are `BIC` or `AIC` or a numeric value +#' If set to `auto` it will do CV to determine best penalty #' @param seed An option to add a seed when training the model #' @param exclude A vector of numbers or covariateId names to exclude from prior #' @param forceIntercept Logical: Force intercept coefficient into regularization #' @param fitBestSubset Logical: Fit final subset with no regularization -#' @param initialRidgeVariance integer or character vector. If set to auto will fit Ridge regression using -#' cross validation to determine best initialRidgeVariance value. +#' @param initialRidgeVariance integer or character vector. If set to `auto` will fit Ridge regression using +#' cross validation to determine best `initialRidgeVariance` value. #' @param tolerance numeric #' @param maxIterations integer #' @param threshold numeric #' @param delta numeric +#' @param nTries If `penalty` is `auto`, how many penalties to include in grid search #' #' @examples #' model.lr <- setLassoLogisticRegression() @@ -194,21 +196,23 @@ setIterativeHardThresholding<- function( tolerance = 1e-08, maxIterations = 10000, threshold = 1e-06, - delta = 0 + delta = 0, + nTries = 10 ){ ensure_installed("IterativeHardThresholding") if(K<1) stop('Invalid maximum number of predictors') - if(!(penalty %in% c("aic", "bic") || is.numeric(penalty))) - stop('Penalty must be "aic", "bic" or numeric') + if(!(penalty %in% c("aic", "bic", "auto") || is.numeric(penalty))) + stop('Penalty must be "aic", "bic", "auto" or numeric') if(!is.logical(forceIntercept)) stop("forceIntercept must be of type: logical") if(!is.logical(fitBestSubset)) stop("fitBestSubset must be of type: logical") if(!inherits(x = seed, what = c('numeric','NULL','integer'))) stop('Invalid seed') + # set seed @@ -242,6 +246,12 @@ setIterativeHardThresholding<- function( name = "Iterative Hard Thresholding" ) + if (penalty == "auto") { + attr(param, 'settings')$manualCV <- TRUE + attr(param, 'settings')$useControl <- FALSE + attr(param, 'settings')$nTries <- nTries + } + attr(param, 'modelType') <- 'binary' attr(param, 'saveType') <- 'RtoJson' From 0c64c2322d4f1f349a25419d3bd228d49c0b8ac0 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 8 Aug 2023 17:13:32 +0200 Subject: [PATCH 02/28] fix hyperParamSearch --- R/CyclopsModels.R | 1 - 1 file changed, 1 deletion(-) diff --git a/R/CyclopsModels.R b/R/CyclopsModels.R index 74816e259..91d8a6def 100644 --- a/R/CyclopsModels.R +++ b/R/CyclopsModels.R @@ -182,7 +182,6 @@ fitCyclopsModel <- function( prediction$evaluationType <- 'Train' # get cv AUC if exists - hyperParamSearch <- data.frame() if(!is.null(modelTrained$cv)){ cvPrediction <- do.call(rbind, lapply(modelTrained$cv, function(x){x$predCV})) cvPrediction$evaluationType <- 'CV' From 6b59ce242720d5d0443791e71c0bae8411179ce8 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 8 Aug 2023 17:37:13 +0200 Subject: [PATCH 03/28] fix notes and warnings --- R/CyclopsModels.R | 18 +++++++++--------- inst/doc/AddingCustomFeatureEngineering.pdf | Bin 225972 -> 0 bytes inst/doc/AddingCustomModels.pdf | Bin 262452 -> 0 bytes inst/doc/AddingCustomSamples.pdf | Bin 225244 -> 0 bytes inst/doc/AddingCustomSplitting.pdf | Bin 222827 -> 0 bytes inst/doc/BuildingDeepLearningModels.pdf | Bin 1203473 -> 0 bytes inst/doc/BuildingEnsembleModels.pdf | Bin 246436 -> 0 bytes inst/doc/BuildingMultiplePredictiveModels.pdf | Bin 217661 -> 0 bytes inst/doc/BuildingPredictiveModels.pdf | Bin 3777230 -> 0 bytes inst/doc/CreatingLearningCurves.pdf | Bin 241574 -> 0 bytes inst/doc/CreatingNetworkstudies.pdf | Bin 176377 -> 0 bytes inst/doc/CreatingShinyApp.pdf | Bin 240199 -> 0 bytes inst/doc/InstallationGuide.pdf | Bin 187632 -> 0 bytes man/setIterativeHardThresholding.Rd | 12 ++++++++---- 14 files changed, 17 insertions(+), 13 deletions(-) delete mode 100644 inst/doc/AddingCustomFeatureEngineering.pdf delete mode 100644 inst/doc/AddingCustomModels.pdf delete mode 100644 inst/doc/AddingCustomSamples.pdf delete mode 100644 inst/doc/AddingCustomSplitting.pdf delete mode 100644 inst/doc/BuildingDeepLearningModels.pdf delete mode 100644 inst/doc/BuildingEnsembleModels.pdf delete mode 100644 inst/doc/BuildingMultiplePredictiveModels.pdf delete mode 100644 inst/doc/BuildingPredictiveModels.pdf delete mode 100644 inst/doc/CreatingLearningCurves.pdf delete mode 100644 inst/doc/CreatingNetworkstudies.pdf delete mode 100644 inst/doc/CreatingShinyApp.pdf delete mode 100644 inst/doc/InstallationGuide.pdf diff --git a/R/CyclopsModels.R b/R/CyclopsModels.R index 91d8a6def..b69c8ec34 100644 --- a/R/CyclopsModels.R +++ b/R/CyclopsModels.R @@ -604,13 +604,13 @@ doCyclopsCVPenalty <- function(data, for (fold in seq_len(nFolds)) { trainData <- data$covariateData$covariates %>% - dplyr::filter(index!=fold) %>% + dplyr::filter(.data$index!=fold) %>% dplyr::select(c("rowId", "covariateId", "covariateValue")) %>% dplyr::collect() trainOutcomes <- data$covariateData$labels %>% - dplyr::filter(index!=fold) %>% - dplyr::select(-index) %>% + dplyr::filter(.data$index!=fold) %>% + dplyr::select(-.data$index) %>% dplyr::collect() cyclopsData <- Cyclops::convertToCyclopsData(outcomes = trainOutcomes, @@ -631,18 +631,18 @@ doCyclopsCVPenalty <- function(data, # predict on held out fold testData <- data$covariateData$covariates %>% - dplyr::filter(index==fold) %>% + dplyr::filter(.data$index==fold) %>% dplyr::select(c("rowId", "covariateId", "covariateValue")) %>% - dplyr::mutate(covariateId=as.numeric(covariateId)) %>% + dplyr::mutate(covariateId=as.numeric(.data$covariateId)) %>% dplyr::collect() testOutcomes <- data$covariateData$labels %>% - dplyr::filter(index==fold) %>% - dplyr::select(-index) %>% + dplyr::filter(.data$index==fold) %>% + dplyr::select(-.data$index) %>% dplyr::collect() - preds <- predict(fit, newCovariates = testData, - newOutcomes = testOutcomes) + preds <- stats::predict(fit, newCovariates = testData, + newOutcomes = testOutcomes) # calculate performance prediction <- data.frame(outcomeCount=testOutcomes$outcomeCount, diff --git a/inst/doc/AddingCustomFeatureEngineering.pdf b/inst/doc/AddingCustomFeatureEngineering.pdf deleted file mode 100644 index e4e8220ce14a089d5314e5bd6a073001c98e7747..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 225972 zcma&MQ;;r9u&&v*ZQC|h+qP|dZ5ylY)wXThwr$(i{4+7H2CMsX`!7c(a! zMsZss7c)^a6M(51jDP@)vx}3NksXZ3#<8XhV2cB(=TzfNkw`nwcsNX72$=|x*Kv+R z+$1lo?avW%qo-|7`xlqPQH0Ga*pxOP(WXZt_mT7bJam^6G-<=j-?cACMlBHsN z9=VVta}%8EPSDmvtc+%MyTw|+-Zvl~&pvM2MA5t%KBM{rAu{yI#UNF`IXRUeKB^zJ 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a/man/setIterativeHardThresholding.Rd b/man/setIterativeHardThresholding.Rd index 5d612f80b..315e6840c 100644 --- a/man/setIterativeHardThresholding.Rd +++ b/man/setIterativeHardThresholding.Rd @@ -15,13 +15,15 @@ setIterativeHardThresholding( tolerance = 1e-08, maxIterations = 10000, threshold = 1e-06, - delta = 0 + delta = 0, + nTries = 10 ) } \arguments{ \item{K}{The maximum number of non-zero predictors} -\item{penalty}{Specifies the IHT penalty; possible values are `BIC` or `AIC` or a numeric value} +\item{penalty}{Specifies the IHT penalty; possible values are `BIC` or `AIC` or a numeric value +If set to `auto` it will do CV to determine best penalty} \item{seed}{An option to add a seed when training the model} @@ -31,8 +33,8 @@ setIterativeHardThresholding( \item{fitBestSubset}{Logical: Fit final subset with no regularization} -\item{initialRidgeVariance}{integer or character vector. If set to auto will fit Ridge regression using -cross validation to determine best initialRidgeVariance value.} +\item{initialRidgeVariance}{integer or character vector. If set to `auto` will fit Ridge regression using +cross validation to determine best `initialRidgeVariance` value.} \item{tolerance}{numeric} @@ -41,6 +43,8 @@ cross validation to determine best initialRidgeVariance value.} \item{threshold}{numeric} \item{delta}{numeric} + +\item{nTries}{If `penalty` is `auto`, how many penalties to include in grid search} } \description{ Create setting for lasso logistic regression From a4b7cd0fd696b0d90a58ff8eb0068f4f5833e359 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 8 Aug 2023 17:40:43 +0200 Subject: [PATCH 04/28] fix docs --- R/CyclopsModels.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/CyclopsModels.R b/R/CyclopsModels.R index b69c8ec34..b372b7903 100644 --- a/R/CyclopsModels.R +++ b/R/CyclopsModels.R @@ -553,8 +553,9 @@ reparamTransferCoefs <- function(inCoefs) { return(coefs) } -#' @details do simple CV to determine best penalty manually +#' do simple CV to determine best penalty manually #' +#' @details #' Will try penalty `BIC` down to `penaltyRatio` * `BIC`. How many penalties #' to try is determined by `nTries.` #' From 4d0bb7f973ecfa370a08d3c2d5e01a680064750c Mon Sep 17 00:00:00 2001 From: egillax Date: Mon, 21 Aug 2023 12:44:09 +0200 Subject: [PATCH 05/28] print out progress --- R/CyclopsModels.R | 10 ++++++++-- tests/testthat/test-cyclopsModels.R | 2 +- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/R/CyclopsModels.R b/R/CyclopsModels.R index b372b7903..0a32ba849 100644 --- a/R/CyclopsModels.R +++ b/R/CyclopsModels.R @@ -556,8 +556,9 @@ reparamTransferCoefs <- function(inCoefs) { #' do simple CV to determine best penalty manually #' #' @details -#' Will try penalty `BIC` down to `penaltyRatio` * `BIC`. How many penalties -#' to try is determined by `nTries.` +#' Will try a sequence of penalties from `BIC` down to `penaltyRatio` * `BIC`. How many penalties +#' to try is determined by `nTries.` Will use cross-validation to determine optimal penalty +#' based on `AUC`. #' #' @param data The training data #' @param prior Cyclops prior to use @@ -602,6 +603,7 @@ doCyclopsCVPenalty <- function(data, hyperParamSearch[i, "Penalty"] <- penalties[i] prior$penalty <- penalties[i] + itStart <- Sys.time() for (fold in seq_len(nFolds)) { trainData <- data$covariateData$covariates %>% @@ -653,6 +655,10 @@ doCyclopsCVPenalty <- function(data, hyperParamSearch[i, paste0("Fold_", fold)] <- auc } hyperParamSearch[i, "avg_CV"] <- mean(as.numeric(hyperParamSearch[i, seq(3,3 + nFolds - 1)])) + itDelta <- Sys.time() - itStart + ParallelLogger::logInfo(paste0('Hyperparameter iteration no: ', i, ' Penalty: ', signif(prior$penalty, 3), + ' AUC: ', signif(hyperParamSearch[i, "avg_CV"], 3), + ' Iteration Time: ', signif(itDelta, 3), " ", attr(itDelta, "units"))) } bestIndex <- which.max(hyperParamSearch$avg_CV) diff --git a/tests/testthat/test-cyclopsModels.R b/tests/testthat/test-cyclopsModels.R index 0bbf6d779..a0969f8e1 100644 --- a/tests/testthat/test-cyclopsModels.R +++ b/tests/testthat/test-cyclopsModels.R @@ -196,7 +196,7 @@ test_that("set IHT inputs", { expect_equal(model_set$param$priorParams$K, k) - penalty <- sample(c('bic', 'aic'),1) + penalty <- sample(c('bic', 'aic', 'auto'),1) model_set <- setIterativeHardThresholding(penalty = penalty) expect_equal(model_set$param$priorParams$penalty, penalty) From 245edf275efa60373656d2c5fefb4c3ac69876f7 Mon Sep 17 00:00:00 2001 From: egillax Date: Mon, 21 Aug 2023 13:59:45 +0200 Subject: [PATCH 06/28] add timing for debugging --- R/RunPlp.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/RunPlp.R b/R/RunPlp.R index 2f9cd0199..a4b45f340 100644 --- a/R/RunPlp.R +++ b/R/RunPlp.R @@ -219,7 +219,7 @@ runPlp <- function( executeSettings = createDefaultExecuteSettings(), saveDirectory = getwd() ){ - + start <- Sys.time() # start log analysisPath <- file.path(saveDirectory, analysisId) logSettings$saveDirectory <- analysisPath @@ -513,7 +513,8 @@ runPlp <- function( tryCatch(savePlpResult(results, file.path(analysisPath,'plpResult')), finally= ParallelLogger::logTrace('Done.')) ParallelLogger::logInfo(paste0('plpResult saved to ..\\', analysisPath ,'\\plpResult')) - + delta <- Sys.time() - start + ParallelLogger::logInfo("RunPLP took ", signif(delta, 3), " ", attr(delta, "units")) return(results) } From 180cea88d885aec72d0961e6b708fbf999fb948e Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 22 Aug 2023 11:50:10 +0200 Subject: [PATCH 07/28] added boruta --- R/Boruta.R | 119 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 119 insertions(+) create mode 100644 R/Boruta.R diff --git a/R/Boruta.R b/R/Boruta.R new file mode 100644 index 000000000..c9fdeca5e --- /dev/null +++ b/R/Boruta.R @@ -0,0 +1,119 @@ +#' Create the settings for Boruta feature selection +#' +#' @details +#' From: https://doi.org/10.18637/jss.v036.i11 +#' +#' @param nJobs How many jobs to do in parallel +#' @param maxDepth Max depth of each tree in the RandomForest used +#' @param nTrees How many trees to use, default is `auto` +#' +#' @return +#' An object of class \code{featureEngineeringSettings} +#' @export +createBorutaFeatureSelection <- function(nJobs = 10L, + maxDepth = 5L, + nTrees = "auto", + verbosity = 2L, + iterations = 100L, + randomState = 42L + ){ + # TODO check python env is correct here + + if (inherits(nJobs, "numeric")) { + nJobs <- as.integer(nJobs) + } + if (inherits(maxDepth, "numeric")) { + maxDepth <- as.integer(maxDepth) + } + if (inherits(nTrees, "numeric")) { + nTrees <- as.integer(nTrees) + } + + checkIsClass(nTrees, c('integer', "character")) + checkIsClass(maxDepth, c('integer')) + if (inherits(nTrees, c("integer"))) { + checkHigher(nTrees, 0) + } else { + if (nTrees != "auto") { + stop("nTrees should be either an integer or 'auto'") + } + } + + checkHigher(maxDepth, 0) + + featureEngineeringSettings <- list( + nTrees = nTrees, + maxDepth = maxDepth + ) + + attr(featureEngineeringSettings, "fun") <- "borutaFeatureSelection" + class(featureEngineeringSettings) <- "featureEngineeringSettings" + + return(featureEngineeringSettings) +} + +borutaFeatureSelection <- function( + trainData, + featureEngineeringSettings, + covariateIdsInclude = NULL +){ + + if(is.null(covariateIdsInclude)){ + #convert data into matrix: + mappedData <- toSparseM(trainData) + + matrixData <- mappedData$dataMatrix + labels <- mappedData$labels + covariateMap <- mappedData$covariateMap + + X <- reticulate::r_to_py(matrixData)$toarray() + y <- reticulate::r_to_py(matrix(labels$outcomeCount, ncol=1)) + + sklearn <- reticulate::import('sklearn') + BorutaPy <- reticulate::import('boruta')$BorutaPy + + + + + rf = sklearn$ensemble$RandomForestClassifier( + max_depth = featureEngineeringSettings$maxDepth, + n_jobs = featureEngineeringSettings$nJobs, + ) + + featureSelector <- BorutaPy(rf, n_estimators=featureEngineeringSettings$nTrees, + verbose=2L, random_state=42L) + + featureSelector$fit(X, y$squeeze()) + + includedFeatures <- featureSelector$support_ + + + covariateIdsInclude <- covariateMap %>% + dplyr::filter(.data$columnId %in% which(includedFeatures)) %>% + dplyr::select("covariateId") %>% dplyr::arrange("covariateId") %>% + dplyr::pull() + } + + trainData$covariateData$covariates <- trainData$covariateData$covariates %>% + dplyr::filter(.data$covariateId %in% covariateIdsInclude) + + trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% + dplyr::filter(.data$covariateId %in% covariateIdsInclude) + + + featureEngineering <- list( + funct = 'borutaFeatureSelection', + settings = list( + featureEngineeringSettings = featureEngineeringSettings, + covariateIdsInclude = covariateIdsInclude + ) + ) + + attr(trainData, 'metaData')$featureEngineering = listAppend( + attr(trainData, 'metaData')$featureEngineering, + featureEngineering + ) + + return(trainData) + +} \ No newline at end of file From 8d94f2de4f80b1373280e5a632171fb4969b4611 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 22 Aug 2023 11:50:44 +0200 Subject: [PATCH 08/28] fix namespace --- NAMESPACE | 1 + 1 file changed, 1 insertion(+) diff --git a/NAMESPACE b/NAMESPACE index f2ba95c9a..7a5c84f4f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -15,6 +15,7 @@ export(computeAuc) export(computeGridPerformance) export(configurePython) export(covariateSummary) +export(createBorutaFeatureSelection) export(createCohortCovariateSettings) export(createDatabaseDetails) export(createDatabaseList) From ae918efb1c62d98375e30cc6fdf05620331c34e8 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 22 Aug 2023 12:59:06 +0200 Subject: [PATCH 09/28] add boruta docs --- man/createBorutaFeatureSelection.Rd | 31 +++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 man/createBorutaFeatureSelection.Rd diff --git a/man/createBorutaFeatureSelection.Rd b/man/createBorutaFeatureSelection.Rd new file mode 100644 index 000000000..d99ec7c41 --- /dev/null +++ b/man/createBorutaFeatureSelection.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Boruta.R +\name{createBorutaFeatureSelection} +\alias{createBorutaFeatureSelection} +\title{Create the settings for Boruta feature selection} +\usage{ +createBorutaFeatureSelection( + nJobs = 10L, + maxDepth = 5L, + nTrees = "auto", + verbosity = 2L, + iterations = 100L, + randomState = 42L +) +} +\arguments{ +\item{nJobs}{How many jobs to do in parallel} + +\item{maxDepth}{Max depth of each tree in the RandomForest used} + +\item{nTrees}{How many trees to use, default is `auto`} +} +\value{ +An object of class \code{featureEngineeringSettings} +} +\description{ +Create the settings for Boruta feature selection +} +\details{ +From: https://doi.org/10.18637/jss.v036.i11 +} From 2273cde280a6011c924f69e51e95ac3ae2fe20b2 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 22 Aug 2023 14:50:07 +0200 Subject: [PATCH 10/28] add python checks and more parameters --- R/Boruta.R | 45 +++++++++++++++++++++++++++++++++++++-------- 1 file changed, 37 insertions(+), 8 deletions(-) diff --git a/R/Boruta.R b/R/Boruta.R index c9fdeca5e..300eb28fe 100644 --- a/R/Boruta.R +++ b/R/Boruta.R @@ -6,19 +6,31 @@ #' @param nJobs How many jobs to do in parallel #' @param maxDepth Max depth of each tree in the RandomForest used #' @param nTrees How many trees to use, default is `auto` +#' @param verbosity 0 for silent, 1 to display iteration number, 2 to display features selected as well +#' @param iterations How many iterations to run `Boruta` for. Default: 100 +#' @param randomState Either `NULL` or an integer. If integer it is the seed used by the random number generator #' #' @return #' An object of class \code{featureEngineeringSettings} #' @export -createBorutaFeatureSelection <- function(nJobs = 10L, +createBorutaFeatureSelection <- function(nJobs = -1L, maxDepth = 5L, nTrees = "auto", verbosity = 2L, iterations = 100L, randomState = 42L ){ - # TODO check python env is correct here + # check python environment + npVersion <- tryCatch({np <- reticulate::import('numpy'); np$`__version__`}, + error = function(e) stop("Numpy must be available in python environment")) + minVersion <- as.integer(substr(npVersion, 3,4)) # extract min version + if (minVersion>= 24) { + stop(paste0("Numpy version must be less than 1.24 for boruta to work, current version is: ", npVersion)) + } + tryCatch(reticulate::import('boruta'), error= function(e) stop("Boruta must be installed in the python environment")) + tryCatch(reticulate::import('sklearn'), error= function(e) stop("sklearn must be installed in the python environment")) + # check input variables if (inherits(nJobs, "numeric")) { nJobs <- as.integer(nJobs) } @@ -28,9 +40,20 @@ createBorutaFeatureSelection <- function(nJobs = 10L, if (inherits(nTrees, "numeric")) { nTrees <- as.integer(nTrees) } + if (inherits(verbosity, "numeric")) { + verbosity <- as.integer(verbosity) + } + if (inherits(iterations, "numeric")) { + iterations <- as.integer(iterations) + } + if (inherits(randomState, "numeric")) { + randomState <- as.integer(randomState) + } + checkIsClass(nTrees, c('integer', "character")) checkIsClass(maxDepth, c('integer')) + checkIsClass(randomState, c("integer", "NULL")) if (inherits(nTrees, c("integer"))) { checkHigher(nTrees, 0) } else { @@ -38,12 +61,19 @@ createBorutaFeatureSelection <- function(nJobs = 10L, stop("nTrees should be either an integer or 'auto'") } } - checkHigher(maxDepth, 0) + checkHigher(iterations, 0) + + if (!verbosity %in% c(0L, 1L, 2L)) { + stop(paste0("verbosity must be one of 0, 1, 2. You supplied: ", verbosity)) + } featureEngineeringSettings <- list( nTrees = nTrees, - maxDepth = maxDepth + maxDepth = maxDepth, + iterations = iterations, + verbosity = verbosity, + randomState = randomState ) attr(featureEngineeringSettings, "fun") <- "borutaFeatureSelection" @@ -72,16 +102,15 @@ borutaFeatureSelection <- function( sklearn <- reticulate::import('sklearn') BorutaPy <- reticulate::import('boruta')$BorutaPy - - - rf = sklearn$ensemble$RandomForestClassifier( max_depth = featureEngineeringSettings$maxDepth, n_jobs = featureEngineeringSettings$nJobs, ) featureSelector <- BorutaPy(rf, n_estimators=featureEngineeringSettings$nTrees, - verbose=2L, random_state=42L) + verbose=featureEngineeringSettings$verbosity, + random_state=featureEngineeringSettings$randomState, + max_iter=featureEngineeringSettings$iterations) featureSelector$fit(X, y$squeeze()) From 5b88f762375bc0957c3a5f1a844aa7a127ad2a72 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 22 Aug 2023 14:55:48 +0200 Subject: [PATCH 11/28] add timing to runPlp --- R/RunPlp.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/RunPlp.R b/R/RunPlp.R index 2f9cd0199..a4b45f340 100644 --- a/R/RunPlp.R +++ b/R/RunPlp.R @@ -219,7 +219,7 @@ runPlp <- function( executeSettings = createDefaultExecuteSettings(), saveDirectory = getwd() ){ - + start <- Sys.time() # start log analysisPath <- file.path(saveDirectory, analysisId) logSettings$saveDirectory <- analysisPath @@ -513,7 +513,8 @@ runPlp <- function( tryCatch(savePlpResult(results, file.path(analysisPath,'plpResult')), finally= ParallelLogger::logTrace('Done.')) ParallelLogger::logInfo(paste0('plpResult saved to ..\\', analysisPath ,'\\plpResult')) - + delta <- Sys.time() - start + ParallelLogger::logInfo("RunPLP took ", signif(delta, 3), " ", attr(delta, "units")) return(results) } From c1f1ca31461522d702aad2be95e8db7d7bafd2b2 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 22 Aug 2023 15:57:47 +0200 Subject: [PATCH 12/28] add missing nJobs --- R/Boruta.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/Boruta.R b/R/Boruta.R index 300eb28fe..7189a279e 100644 --- a/R/Boruta.R +++ b/R/Boruta.R @@ -73,7 +73,8 @@ createBorutaFeatureSelection <- function(nJobs = -1L, maxDepth = maxDepth, iterations = iterations, verbosity = verbosity, - randomState = randomState + randomState = randomState, + nJobs = nJobs ) attr(featureEngineeringSettings, "fun") <- "borutaFeatureSelection" From 43f30c854d86c5aa411ef300753d7cc686162a89 Mon Sep 17 00:00:00 2001 From: egillax Date: Wed, 23 Aug 2023 17:54:30 +0200 Subject: [PATCH 13/28] use sparse boruta --- R/Boruta.R | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/R/Boruta.R b/R/Boruta.R index 7189a279e..8d3d49064 100644 --- a/R/Boruta.R +++ b/R/Boruta.R @@ -21,15 +21,10 @@ createBorutaFeatureSelection <- function(nJobs = -1L, randomState = 42L ){ # check python environment - npVersion <- tryCatch({np <- reticulate::import('numpy'); np$`__version__`}, - error = function(e) stop("Numpy must be available in python environment")) - minVersion <- as.integer(substr(npVersion, 3,4)) # extract min version - if (minVersion>= 24) { - stop(paste0("Numpy version must be less than 1.24 for boruta to work, current version is: ", npVersion)) - } + tryCatch(reticulate::import('numpy'), error = function(e) stop("Numpy must be available in python environment")) tryCatch(reticulate::import('boruta'), error= function(e) stop("Boruta must be installed in the python environment")) tryCatch(reticulate::import('sklearn'), error= function(e) stop("sklearn must be installed in the python environment")) - + # check input variables if (inherits(nJobs, "numeric")) { nJobs <- as.integer(nJobs) @@ -97,7 +92,7 @@ borutaFeatureSelection <- function( labels <- mappedData$labels covariateMap <- mappedData$covariateMap - X <- reticulate::r_to_py(matrixData)$toarray() + X <- reticulate::r_to_py(matrixData) y <- reticulate::r_to_py(matrix(labels$outcomeCount, ncol=1)) sklearn <- reticulate::import('sklearn') From 76d1ac9fae393a6d7224fb1e03cccc8997ec624c Mon Sep 17 00:00:00 2001 From: egillax Date: Wed, 30 Aug 2023 11:42:30 +0200 Subject: [PATCH 14/28] fix python env check --- R/Boruta.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/Boruta.R b/R/Boruta.R index 8d3d49064..42ab1bf05 100644 --- a/R/Boruta.R +++ b/R/Boruta.R @@ -21,7 +21,7 @@ createBorutaFeatureSelection <- function(nJobs = -1L, randomState = 42L ){ # check python environment - tryCatch(reticulate::import('numpy'), error = function(e) stop("Numpy must be available in python environment")) + tryCatch(reticulate::import('numpy'), error = function(e) stop("Numpy must be available in python environment")) tryCatch(reticulate::import('boruta'), error= function(e) stop("Boruta must be installed in the python environment")) tryCatch(reticulate::import('sklearn'), error= function(e) stop("sklearn must be installed in the python environment")) From 81da03aab2c60f671e9cff42383faed74605b164 Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 5 Sep 2023 14:00:47 +0200 Subject: [PATCH 15/28] docs for boruta --- man/createBorutaFeatureSelection.Rd | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/man/createBorutaFeatureSelection.Rd b/man/createBorutaFeatureSelection.Rd index d99ec7c41..f23836e91 100644 --- a/man/createBorutaFeatureSelection.Rd +++ b/man/createBorutaFeatureSelection.Rd @@ -5,7 +5,7 @@ \title{Create the settings for Boruta feature selection} \usage{ createBorutaFeatureSelection( - nJobs = 10L, + nJobs = -1L, maxDepth = 5L, nTrees = "auto", verbosity = 2L, @@ -19,6 +19,12 @@ createBorutaFeatureSelection( \item{maxDepth}{Max depth of each tree in the RandomForest used} \item{nTrees}{How many trees to use, default is `auto`} + +\item{verbosity}{0 for silent, 1 to display iteration number, 2 to display features selected as well} + +\item{iterations}{How many iterations to run `Boruta` for. Default: 100} + +\item{randomState}{Either `NULL` or an integer. If integer it is the seed used by the random number generator} } \value{ An object of class \code{featureEngineeringSettings} From 36e6cb69d09f4d7cae564e5a11732d621bf4ed4e Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Thu, 26 Oct 2023 11:05:16 +0200 Subject: [PATCH 16/28] Update univariate + forward/backward selection --- NAMESPACE | 5 +- R/VariableSelection.R | 385 ++++++++++++++++++++++++------------------ 2 files changed, 225 insertions(+), 165 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index c23d30b02..2db39eada 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -34,7 +34,6 @@ export(createSampleSettings) export(createStudyPopulation) export(createStudyPopulationSettings) export(createTempModelLoc) -export(createUnivariateFeatureSelection) export(createValidationSettings) export(diagnoseMultiplePlp) export(diagnosePlp) @@ -105,13 +104,13 @@ export(setPythonEnvironment) export(setRandomForest) export(setRidgeRegression) export(setSVM) -export(setStepwiseSelection) -export(setUnivariateSelection) export(simulatePlpData) export(sklearnFromJson) export(sklearnToJson) export(splitData) +export(stepwiseSettings) export(toSparseM) +export(univariateSettings) export(validateMultiplePlp) export(viewDatabaseResultPlp) export(viewMultiplePlp) diff --git a/R/VariableSelection.R b/R/VariableSelection.R index 7ff6037c3..e97f891e3 100644 --- a/R/VariableSelection.R +++ b/R/VariableSelection.R @@ -16,216 +16,276 @@ # See the License for the specific language governing permissions and # limitations under the License. -#' Selects features based on univariate statistics -#' -#' @param corMethod which type of correlation to use, `pearson`, `kendall` or `spearman`. default `pearson` -#' @param modelSettings settings of model to use in fit after selecting variables -#' @param nVariables amount of variables to select, default `50` -#' + +#' Create settings using normalized joint mutual information maximization (NJMIM) for feature selection +#' @param k number of variables to select, default `20` #' @export -setUnivariateSelection <- function(modelSettings = PatientLevelPrediction::setLassoLogisticRegression(), - corMethod = "pearson", - nVariables = 50) { # TODO: set dynamic number based on elbow +njmimSettings <- function(k = 20) { - checkIsClass(nVariables, c('numeric','integer')) - if (!corMethod %in% c("pearson", "kendall", "spearman")) { - stop("corMethod needs to be either 'pearson', 'kendall' or 'spearman'") + ensure_installed('praznik') + + checkIsClass(k, c('integer', 'numeric')) + checkHigherEqual(k, 0) + + featureEngineeringSettings <- list(k = k) + + attr(featureEngineeringSettings, "fun") <- "njmimFeatureSelection" + class(featureEngineeringSettings) <- "featureEngineeringSettings" + + return(featureEngineeringSettings) +} + +njmimFeatureSelection <- function(trainData, + featureEngineeringSettings, + covariateIdsInclude = NULL) { + if (is.null(covariateIdsInclude)) { + sparseData <- toSparseM(trainData, trainData$labels) + denseMatrix <- as.matrix(sparseData$dataMatrix) + dataFrame <- as.data.frame(denseMatrix) + y <- sparseData$labels$outcomeCount + + selection <- praznik::NJMIM(X = dataFrame, + Y = y, + k = featureEngineeringSettings$k) + + covariateIdsInclude <- sparseData$covariateMap %>% + dplyr::filter(columnId %in% selection$selection) %>% + dplyr::pull(covariateId) } - # TODO: add class checks input (modelSettings, corMethod)? - param <- list( - modelSettings = modelSettings, - param = list( - corMethod = corMethod, - nVariables = nVariables + trainData$covariateData$covariates <- trainData$covariateData$covariates %>% + dplyr::filter(covariateId %in% covariateIdsInclude) + trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% + dplyr::filter(covariateId %in% covariateIdsInclude) + + featureEngineering <- list( + funct = 'njmimFeatureSelection', + settings = list( + featureEngineeringSettings = featureEngineeringSettings, + covariateIdsInclude = covariateIdsInclude ) ) - result <- list( - fitFunction = "fitUnivariateSelection", - param = param + attr(trainData, 'metaData')$featureEngineering = listAppend( + attr(trainData, 'metaData')$featureEngineering, + featureEngineering ) - class(result) <- "modelSettings" - return(result) + return(trainData) } -fitUnivariateSelection <- function( - trainData, - modelSettings, - search = 'none', - analysisId, - stop = F, - ...) { +#' Create settings using univariate statistics for feature selection +#' @param corMethod which type of correlation to use, `pearson`, `kendall` or `spearman`. default `pearson` +#' @param k number of variables to select, default `20` +#' +#' @export +univariateSettings <- function(k = 20, # TODO: set dynamic number based on elbow + corMethod = "pearson"){ - # Settings variable selection - param <- modelSettings$param$param + if (inherits(k, 'numeric')) { + k <- as.integer(k) + } - covariates <- as.data.frame(trainData$covariateData$covariates) + checkIsClass(k, 'integer') + checkHigherEqual(k, 0) - if (length(unique(covariates$covariateId)) > param$nVariables) { - outcomes <- trainData$labels[, c("rowId", "outcomeCount")] - - # Select features based on univariate association with outcome - correlation <- sapply(unique(covariates$covariateId), function(covId) { - stats::cor(ifelse(outcomes$rowId %in% covariates$rowId[covariates$covariateId == covId], 1, 0), # TODO: can this be done smarter for sparse data - outcomes$outcomeCount, - method = param$corMethod) - }) - names(correlation) <- unique(covariates$covariateId) - - # Order from high to low absolute correlation - correlation <- correlation[order(abs(correlation), decreasing = TRUE)] - - # Select variables - selected <- names(correlation)[1:min(param$nVariables, length(correlation))] - - # Update covariate data - trainData$covariateData <- updateCovariateData(trainData$covariateData, covIds=selected, update="select") + if (!corMethod %in% c("pearson", "kendall", "spearman")) { + stop("corMethod needs to be either 'pearson', 'kendall' or 'spearman'") + } + + featureEngineeringSettings <- list(corMethod = corMethod, + k = k) + + attr(featureEngineeringSettings, "fun") <- "univariateFeatureSelection" + class(featureEngineeringSettings) <- "featureEngineeringSettings" + + return(featureEngineeringSettings) +} + + +univariateFeatureSelection <- function(trainData, + featureEngineeringSettings, + covariateIdsInclude = NULL) { + if (is.null(covariateIdsInclude)) { + sparseData <- toSparseM(trainData, trainData$labels) + denseMatrix <- as.matrix(sparseData$dataMatrix) + dataFrame <- as.data.frame(denseMatrix) + y <- sparseData$labels$outcomeCount - ParallelLogger::logTrace('Finished variable selection.') - } else { - ParallelLogger::logTrace('No variable selection, number of covariates less than or equal to nVariables.') + if (ncol(dataFrame) > featureEngineeringSettings$k) { + # Select features based on univariate association with outcome + correlation <- sapply(1:ncol(dataFrame), function(col) { + stats::cor(dataFrame[,col], y, method = featureEngineeringSettings$corMethod) + }) + names(correlation) <- 1:ncol(dataFrame) + + # Order from high to low absolute correlation + correlation <- correlation[order(abs(correlation), decreasing = TRUE)] + + # Select variables + selected <- names(correlation)[1:min(featureEngineeringSettings$k, length(correlation))] + + covariateIdsInclude <- sparseData$covariateMap %>% + dplyr::filter(columnId %in% selected) %>% + dplyr::pull(covariateId) + + } } - # Fit final PLP model - fun <- eval(parse(text = modelSettings$param$modelSettings$fitFunction)) - args <- list( - trainData = trainData, - modelSettings = modelSettings$param$modelSettings, - search = search, - analysisId = analysisId + trainData$covariateData$covariates <- trainData$covariateData$covariates %>% + dplyr::filter(covariateId %in% covariateIdsInclude) + trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% + dplyr::filter(covariateId %in% covariateIdsInclude) + + featureEngineering <- list( + funct = 'univariateFeatureSelection', + settings = list( + featureEngineeringSettings = featureEngineeringSettings, + covariateIdsInclude = covariateIdsInclude + ) + ) + + attr(trainData, 'metaData')$featureEngineering = listAppend( + attr(trainData, 'metaData')$featureEngineering, + featureEngineering ) - plpModel <- do.call(fun, args) - return(plpModel) + return(trainData) } -#' Selects features using stepwise selection -#' -#' @param modelSettings settings of model to use in fit after selecting variables + + +#' Create settings using stepwise selection for feature selection +#' @param k number of variables to select, default `20` #' @param selectMethod `backward` or `forward` selection -#' @param nInitialVariables # of variables to select initially -#' @param nVariables amount of variables to select, default `20` +#' @param kStart number of variables to select initially #' @param stepSize How many variables to add/remove in each step -#' +#' @param modelSettings settings of model to use in fit after selecting variables #' @export -setStepwiseSelection <- function(modelSettings, - selectMethod = "backward", - nInitialVariables = 50, # TODO: set dynamic number based on elbow - nVariables = 20, # TODO: set dynamic number based on elbow - stepSize = 1) { - - checkIsClass(nInitialVariables, c('numeric','integer')) - checkIsClass(nVariables, c('numeric','integer')) - checkIsClass(stepSize, c('numeric','integer')) +stepwiseSettings <- function(k = 20, # TODO: set dynamic number based on elbow + selectMethod = "backward", + kStart = 100, # TODO: set dynamic number based on elbow + stepSize = 1, + modelSettings = PatientLevelPrediction::setLassoLogisticRegression()) { + + checkIsClass(k, c('integer', 'numeric')) + checkHigherEqual(k, 0) + if (!selectMethod %in% c("forward", "backward")) { stop("selectMethod needs to be either 'forward' or 'backward") } - # TODO: add class checks input (modelSettings, selectMethod)? - - param <- list( - modelSettings = PatientLevelPrediction::setLassoLogisticRegression(), - param = list( - selectMethod = selectMethod, - nInitialVariables = nInitialVariables, # For classifiers with initial selection based on variable importance - nVariables = nVariables, - stepSize = stepSize # Number of variables removed at the same time - ) - ) - result <- list( - fitFunction = "fitStepwiseSelection", - param = param - ) - class(result) <- "modelSettings" + checkIsClass(kStart, c('integer', 'numeric')) + checkHigherEqual(kStart, 0) - return(result) + checkIsClass(stepSize, c('integer', 'numeric')) + checkHigherEqual(stepSize, 0) + + featureEngineeringSettings <- list(k = k, + selectMethod = selectMethod, + kStart = kStart, + stepSize = stepSize, + modelSettings = modelSettings) + + attr(featureEngineeringSettings, "fun") <- "stepwiseFeatureSelection" + class(featureEngineeringSettings) <- "featureEngineeringSettings" + + return(featureEngineeringSettings) } -fitStepwiseSelection <- function( - trainData, - modelSettings, - search = 'none', - analysisId, - stop = F, - ...) { - - # Initial fit PLP model - fun <- eval(parse(text = modelSettings$param$modelSettings$fitFunction)) - args <- list( - trainData = trainData, - modelSettings = modelSettings$param$modelSettings, - search = search, - analysisId = analysisId - ) - plpModel <- do.call(fun, args) - - if (modelSettings$param$param$selectMethod == "backward") { - # Initial selection - # Select non-zero coefficients for LASSO - covIds <- plpModel$model$coefficients$covariateIds[plpModel$model$coefficients$betas != 0] +stepwiseFeatureSelection <- function(trainData, + featureEngineeringSettings, + covariateIdsInclude = NULL) { + if (is.null(covariateIdsInclude)) { + search <- 999 # TODO: which number to use? + analysisId <- 999 # TODO: which number to use? - # TODO: make generic across algorithms based on var importance (e.g. randomForest) - # covIds <- TODO + # Initial fit PLP model + fun <- eval(parse(text = featureEngineeringSettings$modelSettings$fitFunction)) + args <- list( + trainData = trainData, + modelSettings = featureEngineeringSettings$modelSettings, + search = search, + analysisId = analysisId + ) + plpModel <- do.call(fun, args) - fullCovariates <- NULL - updateIteration <- "remove" + if (featureEngineeringSettings$selectMethod == "backward") { + # Initial selection + # Select non-zero coefficients for LASSO + covIds <- plpModel$model$coefficients$covariateIds[plpModel$model$coefficients$betas != 0] + + # TODO: make generic across algorithms based on var importance (e.g. randomForest) + # covIds <- TODO + + fullCovariates <- NULL + updateIteration <- "remove" + + } else if (featureEngineeringSettings$selectMethod == "forward") { + # Initial selection (empty) + covIds <- NULL + + fullCovariates <- as.data.frame(trainData$covariateData$covariates) + updateIteration <- "add" + + } else { + stop("Variable selection stopped: selectMethod not implemented.") + } - } else if (modelSettings$param$param$selectMethod == "forward") { - # Initial selection (empty) - covIds <- NULL + update <- "select" # Only first update of covariates + update_trainData <- trainData - fullCovariates <- as.data.frame(trainData$covariateData$covariates) - updateIteration <- "add" + while(!is.null(covIds) | update == "select") { # Stop when selected is NULL + # Update covariate data + update_trainData$covariateData <- updateCovariateData(update_trainData$covariateData, covIds=covIds, update=update, fullCovariates=fullCovariates) + + # Backward or forward select variables + update <- updateIteration + covIds <- selectVariables(featureEngineeringSettings, update_trainData, fullCovariates, search, analysisId) + } - } else { - stop("Variable selection stopped: selectMethod not implemented.") + covariates <- as.data.frame(update_trainData$covariateData$covariates) + covariateIdsInclude <- unique(covariates$covariateId) } - update <- "select" # Only first update of covariates - while(!is.null(covIds) | update == "select") { # Stop when selected is NULL - # Update covariate data - trainData$covariateData <- updateCovariateData(trainData$covariateData, covIds=covIds, update=update, fullCovariates=fullCovariates) - - # Backward or forward select variables - update <- updateIteration - covIds <- selectVariables(modelSettings, trainData, fullCovariates, search, analysisId) - } + trainData$covariateData$covariates <- trainData$covariateData$covariates %>% + dplyr::filter(covariateId %in% covariateIdsInclude) + trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% + dplyr::filter(covariateId %in% covariateIdsInclude) - ParallelLogger::logTrace('Finished variable selection.') + featureEngineering <- list( + funct = 'univariateFeatureSelection', + settings = list( + featureEngineeringSettings = featureEngineeringSettings, + covariateIdsInclude = covariateIdsInclude + ) + ) - # Fit final PLP model - fun <- eval(parse(text = modelSettings$param$modelSettings$fitFunction)) - args <- list( - trainData = trainData, - modelSettings = modelSettings$param$modelSettings, - search = search, - analysisId = analysisId + attr(trainData, 'metaData')$featureEngineering = listAppend( + attr(trainData, 'metaData')$featureEngineering, + featureEngineering ) - plpModel <- do.call(fun, args) - return(plpModel) + return(trainData) } -selectVariables <- function(modelSettings, trainData, fullCovariates, search, analysisId) { +# Help function stepwiseFeatureSelection +selectVariables <- function(featureEngineeringSettings, trainData, fullCovariates, search, analysisId) { # Settings variable selection - param <- modelSettings$param$param - covariates <- as.data.frame(trainData$covariateData$covariates) - if (param$selectMethod == "backward") { + if (featureEngineeringSettings$selectMethod == "backward") { update <- "remove" - start <- (length(unique(covariates$covariateId)) > param$nVariables) # Too many variables + start <- (length(unique(covariates$covariateId)) > featureEngineeringSettings$k) # Too many variables covariateList <- unique(covariates$covariateId) - } else if (param$selectMethod == "forward") { + } else if (featureEngineeringSettings$selectMethod == "forward") { update <- "add" - start <- (length(unique(covariates$covariateId)) < param$nVariables) # Not enough variables + start <- (length(unique(covariates$covariateId)) < featureEngineeringSettings$k) # Not enough variables covariateList <- unique(fullCovariates$covariateId)[!(unique(fullCovariates$covariateId) %in% unique(covariates$covariateId))] } - # TODO: make correction for param$stepSize to come to exactly the right number of variables + # TODO: make correction for featureEngineeringSettings$stepSize to come to exactly the right number of variables if (start) { performance <- sapply(covariateList, function(covId) { @@ -234,10 +294,10 @@ selectVariables <- function(modelSettings, trainData, fullCovariates, search, an tempData$covariateData <- updateCovariateData(tempData$covariateData, covIds=covId, update=update, fullCovariates=fullCovariates) # Re-fit PLP model - fun <- eval(parse(text = modelSettings$param$modelSettings$fitFunction)) + fun <- eval(parse(text = featureEngineeringSettings$modelSettings$fitFunction)) args <- list( trainData = tempData, - modelSettings = modelSettings$param$modelSettings, + modelSettings = featureEngineeringSettings$modelSettings, search = search, analysisId = analysisId ) @@ -251,19 +311,19 @@ selectVariables <- function(modelSettings, trainData, fullCovariates, search, an }) names(performance) <- covariateList - if (param$selectMethod == "backward") { + if (featureEngineeringSettings$selectMethod == "backward") { # Order from low to high performance (minimum negative log likelihood is high) performance <- performance[order(performance, decreasing = TRUE)] # Select variables to remove - covIds <- names(performance)[1:min(param$stepSize, length(performance))] + covIds <- names(performance)[1:min(featureEngineeringSettings$stepSize, length(performance))] - } else if (param$selectMethod == "forward") { + } else if (featureEngineeringSettings$selectMethod == "forward") { # Order from high to low performance (minimum negative log likelihood is high) performance <- performance[order(performance, decreasing = FALSE)] # Select variables to add - covIds <- names(performance)[1:min(param$stepSize, length(performance))] + covIds <- names(performance)[1:min(featureEngineeringSettings$stepSize, length(performance))] } return(covIds) @@ -272,6 +332,7 @@ selectVariables <- function(modelSettings, trainData, fullCovariates, search, an return(NULL) # Return NULL to initiate stop } +# Help function stepwiseFeatureSelection updateCovariateData <- function(covariateData, covIds, update="select", fullCovariates=NULL) { # FeatureExtraction -> excludedCovariateConceptIds: A list of concept IDs that should NOT be used to construct covariates. newCovariates <- as.data.frame(covariateData$covariates) # TODO: try without From 264c620855a6efd52fc9767dcd075de51a1068ba Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Thu, 26 Oct 2023 11:05:36 +0200 Subject: [PATCH 17/28] Move NJMIM to main script --- R/NJMIM.R | 74 ------------------------------------------------------- 1 file changed, 74 deletions(-) delete mode 100644 R/NJMIM.R diff --git a/R/NJMIM.R b/R/NJMIM.R deleted file mode 100644 index d55a6538b..000000000 --- a/R/NJMIM.R +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright 2023 Observational Health Data Sciences and Informatics -# -# This file is part of PatientLevelPrediction -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -#' Create settings using normalized joint mutual information maximization (NJMIM) for feature selection -#' @param k number of features to select -#' @export -njmimSettings <- function(k=20) { - - ensure_installed('praznik') - - checkIsClass(k, c('integer', 'numeric')) - checkHigherEqual(k, 0) - - featureEngineeringSettings <- list(k = k) - - attr(featureEngineeringSettings, "fun") <- "njmimFeatureSelection" - class(featureEngineeringSettings) <- "featureEngineeringSettings" - - return(featureEngineeringSettings) -} - -njmimFeatureSelection <- function(trainData, - featureEngineeringSettings, - covariateIdsInclude = NULL) { - if (is.null(covariateIdsInclude)) { - sparseData <- toSparseM(trainData, trainData$labels) - denseMatrix <- as.matrix(sparseData$dataMatrix) - dataFrame <- as.data.frame(denseMatrix) - y <- sparseData$labels$outcomeCount - - selection <- praznik::NJMIM(X = dataFrame, - Y = y, - k = featureEngineeringSettings$k) - - covariateIdsInclude <- sparseData$covariateMap %>% - dplyr::filter(columnId %in% selection$selection) %>% - dplyr::pull(covariateId) - } - - - trainData$covariateData$covariates <- trainData$covariateData$covariates %>% - dplyr::filter(covariateId %in% covariateIdsInclude) - trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% - dplyr::filter(covariateId %in% covariateIdsInclude) - - featureEngineering <- list( - funct = 'njmimFeatureSelection', - settings = list( - featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = covariateIdsInclude - ) - ) - - attr(trainData, 'metaData')$featureEngineering = listAppend( - attr(trainData, 'metaData')$featureEngineering, - featureEngineering - ) - - return(trainData) -} \ No newline at end of file From a94ca739109d9ae200732575c8233c518a480060 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Thu, 26 Oct 2023 11:05:49 +0200 Subject: [PATCH 18/28] Remove python univariate selection --- R/FeatureEngineering.R | 158 ++++++++++++++++++++--------------------- 1 file changed, 78 insertions(+), 80 deletions(-) diff --git a/R/FeatureEngineering.R b/R/FeatureEngineering.R index e3d0eee44..0f40d38b1 100644 --- a/R/FeatureEngineering.R +++ b/R/FeatureEngineering.R @@ -10,7 +10,6 @@ # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. @@ -51,24 +50,23 @@ createFeatureEngineeringSettings <- function(type = 'none'){ #' #' @return #' An object of class \code{featureEngineeringSettings} -#' @export -createUnivariateFeatureSelection <- function(k = 100){ - - if (inherits(k, 'numeric')) { - k <- as.integer(k) - } - - checkIsClass(k, 'integer') - checkHigherEqual(k, 0) - - featureEngineeringSettings <- list(k = k) - - attr(featureEngineeringSettings, "fun") <- "univariateFeatureSelection" - class(featureEngineeringSettings) <- "featureEngineeringSettings" - - return(featureEngineeringSettings) - -} +# createUnivariateFeatureSelection <- function(k = 100){ +# +# if (inherits(k, 'numeric')) { +# k <- as.integer(k) +# } +# +# checkIsClass(k, 'integer') +# checkHigherEqual(k, 0) +# +# featureEngineeringSettings <- list(k = k) +# +# attr(featureEngineeringSettings, "fun") <- "univariateFeatureSelection" +# class(featureEngineeringSettings) <- "featureEngineeringSettings" +# +# return(featureEngineeringSettings) +# +# } #' Create the settings for random foreat based feature selection #' @@ -99,66 +97,66 @@ createRandomForestFeatureSelection <- function(ntrees = 2000, maxDepth = 17){ return(featureEngineeringSettings) } -univariateFeatureSelection <- function( - trainData, - featureEngineeringSettings, - covariateIdsInclude = NULL){ - - if(is.null(covariateIdsInclude)){ - #convert data into matrix: - mappedData <- toSparseM(trainData, trainData$labels) - - matrixData <- mappedData$dataMatrix - labels <- mappedData$labels - covariateMap <- mappedData$covariateMap - - X <- reticulate::r_to_py(matrixData) - y <- reticulate::r_to_py(labels[,'outcomeCount']) - - np <- reticulate::import('numpy') - os <- reticulate::import('os') - sys <- reticulate::import('sys') - math <- reticulate::import('math') - scipy <- reticulate::import('scipy') - - sklearn <- reticulate::import('sklearn') - - SelectKBest <- sklearn$feature_selection$SelectKBest - chi2 <- sklearn$feature_selection$chi2 - - kbest <- SelectKBest(chi2, k = featureEngineeringSettings$k)$fit(X, y$outcomeCount) - kbest$scores_ <- np$nan_to_num(kbest$scores_) - - # taken from sklearn code, matches the application during transform call - k <- featureEngineeringSettings$k - mask <- np$zeros(length(kbest$scores_), dtype='bool') - mask[np$argsort(kbest$scores_, kind="mergesort")+1][(length(kbest$scores_)-k+1):length(kbest$scores_)] <- TRUE - - covariateIdsInclude <- covariateMap[mask,]$covariateId - } - - trainData$covariateData$covariates <- trainData$covariateData$covariates %>% - dplyr::filter(.data$covariateId %in% covariateIdsInclude) - - trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% - dplyr::filter(.data$covariateId %in% covariateIdsInclude) - - featureEngineering <- list( - funct = 'univariateFeatureSelection', - settings = list( - featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = covariateIdsInclude - ) - ) - - attr(trainData, 'metaData')$featureEngineering = listAppend( - attr(trainData, 'metaData')$featureEngineering, - featureEngineering - ) - - return(trainData) - -} +# univariateFeatureSelection <- function( +# trainData, +# featureEngineeringSettings, +# covariateIdsInclude = NULL){ +# +# if(is.null(covariateIdsInclude)){ +# #convert data into matrix: +# mappedData <- toSparseM(trainData, trainData$labels) +# +# matrixData <- mappedData$dataMatrix +# labels <- mappedData$labels +# covariateMap <- mappedData$covariateMap +# +# X <- reticulate::r_to_py(matrixData) +# y <- reticulate::r_to_py(labels[,'outcomeCount']) +# +# np <- reticulate::import('numpy') +# os <- reticulate::import('os') +# sys <- reticulate::import('sys') +# math <- reticulate::import('math') +# scipy <- reticulate::import('scipy') +# +# sklearn <- reticulate::import('sklearn') +# +# SelectKBest <- sklearn$feature_selection$SelectKBest +# chi2 <- sklearn$feature_selection$chi2 +# +# kbest <- SelectKBest(chi2, k = featureEngineeringSettings$k)$fit(X, y$outcomeCount) +# kbest$scores_ <- np$nan_to_num(kbest$scores_) +# +# # taken from sklearn code, matches the application during transform call +# k <- featureEngineeringSettings$k +# mask <- np$zeros(length(kbest$scores_), dtype='bool') +# mask[np$argsort(kbest$scores_, kind="mergesort")+1][(length(kbest$scores_)-k+1):length(kbest$scores_)] <- TRUE +# +# covariateIdsInclude <- covariateMap[mask,]$covariateId +# } +# +# trainData$covariateData$covariates <- trainData$covariateData$covariates %>% +# dplyr::filter(.data$covariateId %in% covariateIdsInclude) +# +# trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% +# dplyr::filter(.data$covariateId %in% covariateIdsInclude) +# +# featureEngineering <- list( +# funct = 'univariateFeatureSelection', +# settings = list( +# featureEngineeringSettings = featureEngineeringSettings, +# covariateIdsInclude = covariateIdsInclude +# ) +# ) +# +# attr(trainData, 'metaData')$featureEngineering = listAppend( +# attr(trainData, 'metaData')$featureEngineering, +# featureEngineering +# ) +# +# return(trainData) +# +# } randomForestFeatureSelection <- function( @@ -231,7 +229,7 @@ randomForestFeatureSelection <- function( } - +# Help function to apply any feature engineering method featureEngineer <- function(data, featureEngineeringSettings){ ParallelLogger::logInfo('Starting Feature Engineering') From 7a0b46191911b6caafdb87a82b23a6bac9842682 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Thu, 26 Oct 2023 11:24:20 +0200 Subject: [PATCH 19/28] Add boruta to file variable selection --- NAMESPACE | 2 +- R/Boruta.R | 144 ------------------------------------------ R/VariableSelection.R | 144 ++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 145 insertions(+), 145 deletions(-) delete mode 100644 R/Boruta.R diff --git a/NAMESPACE b/NAMESPACE index b846d51f0..65e133512 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,13 +9,13 @@ export(addMultipleDiagnosePlpToDatabase) export(addMultipleRunPlpToDatabase) export(addRunPlpToDatabase) export(averagePrecision) +export(borutaSettings) export(brierScore) export(calibrationLine) export(computeAuc) export(computeGridPerformance) export(configurePython) export(covariateSummary) -export(createBorutaFeatureSelection) export(createCohortCovariateSettings) export(createDatabaseDetails) export(createDatabaseList) diff --git a/R/Boruta.R b/R/Boruta.R deleted file mode 100644 index 42ab1bf05..000000000 --- a/R/Boruta.R +++ /dev/null @@ -1,144 +0,0 @@ -#' Create the settings for Boruta feature selection -#' -#' @details -#' From: https://doi.org/10.18637/jss.v036.i11 -#' -#' @param nJobs How many jobs to do in parallel -#' @param maxDepth Max depth of each tree in the RandomForest used -#' @param nTrees How many trees to use, default is `auto` -#' @param verbosity 0 for silent, 1 to display iteration number, 2 to display features selected as well -#' @param iterations How many iterations to run `Boruta` for. Default: 100 -#' @param randomState Either `NULL` or an integer. If integer it is the seed used by the random number generator -#' -#' @return -#' An object of class \code{featureEngineeringSettings} -#' @export -createBorutaFeatureSelection <- function(nJobs = -1L, - maxDepth = 5L, - nTrees = "auto", - verbosity = 2L, - iterations = 100L, - randomState = 42L - ){ - # check python environment - tryCatch(reticulate::import('numpy'), error = function(e) stop("Numpy must be available in python environment")) - tryCatch(reticulate::import('boruta'), error= function(e) stop("Boruta must be installed in the python environment")) - tryCatch(reticulate::import('sklearn'), error= function(e) stop("sklearn must be installed in the python environment")) - - # check input variables - if (inherits(nJobs, "numeric")) { - nJobs <- as.integer(nJobs) - } - if (inherits(maxDepth, "numeric")) { - maxDepth <- as.integer(maxDepth) - } - if (inherits(nTrees, "numeric")) { - nTrees <- as.integer(nTrees) - } - if (inherits(verbosity, "numeric")) { - verbosity <- as.integer(verbosity) - } - if (inherits(iterations, "numeric")) { - iterations <- as.integer(iterations) - } - if (inherits(randomState, "numeric")) { - randomState <- as.integer(randomState) - } - - - checkIsClass(nTrees, c('integer', "character")) - checkIsClass(maxDepth, c('integer')) - checkIsClass(randomState, c("integer", "NULL")) - if (inherits(nTrees, c("integer"))) { - checkHigher(nTrees, 0) - } else { - if (nTrees != "auto") { - stop("nTrees should be either an integer or 'auto'") - } - } - checkHigher(maxDepth, 0) - checkHigher(iterations, 0) - - if (!verbosity %in% c(0L, 1L, 2L)) { - stop(paste0("verbosity must be one of 0, 1, 2. You supplied: ", verbosity)) - } - - featureEngineeringSettings <- list( - nTrees = nTrees, - maxDepth = maxDepth, - iterations = iterations, - verbosity = verbosity, - randomState = randomState, - nJobs = nJobs - ) - - attr(featureEngineeringSettings, "fun") <- "borutaFeatureSelection" - class(featureEngineeringSettings) <- "featureEngineeringSettings" - - return(featureEngineeringSettings) -} - -borutaFeatureSelection <- function( - trainData, - featureEngineeringSettings, - covariateIdsInclude = NULL -){ - - if(is.null(covariateIdsInclude)){ - #convert data into matrix: - mappedData <- toSparseM(trainData) - - matrixData <- mappedData$dataMatrix - labels <- mappedData$labels - covariateMap <- mappedData$covariateMap - - X <- reticulate::r_to_py(matrixData) - y <- reticulate::r_to_py(matrix(labels$outcomeCount, ncol=1)) - - sklearn <- reticulate::import('sklearn') - BorutaPy <- reticulate::import('boruta')$BorutaPy - - rf = sklearn$ensemble$RandomForestClassifier( - max_depth = featureEngineeringSettings$maxDepth, - n_jobs = featureEngineeringSettings$nJobs, - ) - - featureSelector <- BorutaPy(rf, n_estimators=featureEngineeringSettings$nTrees, - verbose=featureEngineeringSettings$verbosity, - random_state=featureEngineeringSettings$randomState, - max_iter=featureEngineeringSettings$iterations) - - featureSelector$fit(X, y$squeeze()) - - includedFeatures <- featureSelector$support_ - - - covariateIdsInclude <- covariateMap %>% - dplyr::filter(.data$columnId %in% which(includedFeatures)) %>% - dplyr::select("covariateId") %>% dplyr::arrange("covariateId") %>% - dplyr::pull() - } - - trainData$covariateData$covariates <- trainData$covariateData$covariates %>% - dplyr::filter(.data$covariateId %in% covariateIdsInclude) - - trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% - dplyr::filter(.data$covariateId %in% covariateIdsInclude) - - - featureEngineering <- list( - funct = 'borutaFeatureSelection', - settings = list( - featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = covariateIdsInclude - ) - ) - - attr(trainData, 'metaData')$featureEngineering = listAppend( - attr(trainData, 'metaData')$featureEngineering, - featureEngineering - ) - - return(trainData) - -} \ No newline at end of file diff --git a/R/VariableSelection.R b/R/VariableSelection.R index e97f891e3..0956b0086 100644 --- a/R/VariableSelection.R +++ b/R/VariableSelection.R @@ -365,3 +365,147 @@ updateCovariateData <- function(covariateData, covIds, update="select", fullCova } +#' Create the settings for Boruta feature selection +#' +#' @details +#' From: https://doi.org/10.18637/jss.v036.i11 +#' +#' @param nJobs How many jobs to do in parallel +#' @param maxDepth Max depth of each tree in the RandomForest used +#' @param nTrees How many trees to use, default is `auto` +#' @param verbosity 0 for silent, 1 to display iteration number, 2 to display features selected as well +#' @param iterations How many iterations to run `Boruta` for. Default: 100 +#' @param randomState Either `NULL` or an integer. If integer it is the seed used by the random number generator +#' +#' @return +#' An object of class \code{featureEngineeringSettings} +#' @export +borutaSettings <- function(nJobs = -1L, + maxDepth = 5L, + nTrees = "auto", + verbosity = 2L, + iterations = 100L, + randomState = 42L +){ + # check python environment + tryCatch(reticulate::import('numpy'), error = function(e) stop("Numpy must be available in python environment")) + tryCatch(reticulate::import('boruta'), error= function(e) stop("Boruta must be installed in the python environment")) + tryCatch(reticulate::import('sklearn'), error= function(e) stop("sklearn must be installed in the python environment")) + + # check input variables + if (inherits(nJobs, "numeric")) { + nJobs <- as.integer(nJobs) + } + if (inherits(maxDepth, "numeric")) { + maxDepth <- as.integer(maxDepth) + } + if (inherits(nTrees, "numeric")) { + nTrees <- as.integer(nTrees) + } + if (inherits(verbosity, "numeric")) { + verbosity <- as.integer(verbosity) + } + if (inherits(iterations, "numeric")) { + iterations <- as.integer(iterations) + } + if (inherits(randomState, "numeric")) { + randomState <- as.integer(randomState) + } + + + checkIsClass(nTrees, c('integer', "character")) + checkIsClass(maxDepth, c('integer')) + checkIsClass(randomState, c("integer", "NULL")) + if (inherits(nTrees, c("integer"))) { + checkHigher(nTrees, 0) + } else { + if (nTrees != "auto") { + stop("nTrees should be either an integer or 'auto'") + } + } + checkHigher(maxDepth, 0) + checkHigher(iterations, 0) + + if (!verbosity %in% c(0L, 1L, 2L)) { + stop(paste0("verbosity must be one of 0, 1, 2. You supplied: ", verbosity)) + } + + featureEngineeringSettings <- list( + nTrees = nTrees, + maxDepth = maxDepth, + iterations = iterations, + verbosity = verbosity, + randomState = randomState, + nJobs = nJobs + ) + + attr(featureEngineeringSettings, "fun") <- "borutaFeatureSelection" + class(featureEngineeringSettings) <- "featureEngineeringSettings" + + return(featureEngineeringSettings) +} + +borutaFeatureSelection <- function( + trainData, + featureEngineeringSettings, + covariateIdsInclude = NULL +){ + + if(is.null(covariateIdsInclude)){ + #convert data into matrix: + mappedData <- toSparseM(trainData) + + matrixData <- mappedData$dataMatrix + labels <- mappedData$labels + covariateMap <- mappedData$covariateMap + + X <- reticulate::r_to_py(matrixData) + y <- reticulate::r_to_py(matrix(labels$outcomeCount, ncol=1)) + + sklearn <- reticulate::import('sklearn') + BorutaPy <- reticulate::import('boruta')$BorutaPy + + rf = sklearn$ensemble$RandomForestClassifier( + max_depth = featureEngineeringSettings$maxDepth, + n_jobs = featureEngineeringSettings$nJobs, + ) + + featureSelector <- BorutaPy(rf, n_estimators=featureEngineeringSettings$nTrees, + verbose=featureEngineeringSettings$verbosity, + random_state=featureEngineeringSettings$randomState, + max_iter=featureEngineeringSettings$iterations) + + featureSelector$fit(X, y$squeeze()) + + includedFeatures <- featureSelector$support_ + + + covariateIdsInclude <- covariateMap %>% + dplyr::filter(.data$columnId %in% which(includedFeatures)) %>% + dplyr::select("covariateId") %>% dplyr::arrange("covariateId") %>% + dplyr::pull() + } + + trainData$covariateData$covariates <- trainData$covariateData$covariates %>% + dplyr::filter(.data$covariateId %in% covariateIdsInclude) + + trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% + dplyr::filter(.data$covariateId %in% covariateIdsInclude) + + + featureEngineering <- list( + funct = 'borutaFeatureSelection', + settings = list( + featureEngineeringSettings = featureEngineeringSettings, + covariateIdsInclude = covariateIdsInclude + ) + ) + + attr(trainData, 'metaData')$featureEngineering = listAppend( + attr(trainData, 'metaData')$featureEngineering, + featureEngineering + ) + + return(trainData) + +} From e829364d304b2b06bd47053368807b9931732581 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Thu, 26 Oct 2023 11:29:40 +0200 Subject: [PATCH 20/28] Update docs --- ...aFeatureSelection.Rd => borutaSettings.Rd} | 8 ++-- man/createRandomForestFeatureSelection.Rd | 11 +++++- man/createUnivariateFeatureSelection.Rd | 20 ---------- man/doCyclopsCVPenalty.Rd | 37 +++++++++++++++++++ man/loadTrainTestData.Rd | 27 ++++++++++++++ man/njmimSettings.Rd | 4 +- man/saveTrainTestData.Rd | 28 ++++++++++++++ man/setStepwiseSelection.Rd | 28 -------------- man/setUnivariateSelection.Rd | 22 ----------- man/stepwiseSettings.Rd | 28 ++++++++++++++ man/univariateSettings.Rd | 16 ++++++++ 11 files changed, 151 insertions(+), 78 deletions(-) rename man/{createBorutaFeatureSelection.Rd => borutaSettings.Rd} (85%) delete mode 100644 man/createUnivariateFeatureSelection.Rd create mode 100644 man/doCyclopsCVPenalty.Rd create mode 100644 man/loadTrainTestData.Rd create mode 100644 man/saveTrainTestData.Rd delete mode 100644 man/setStepwiseSelection.Rd delete mode 100644 man/setUnivariateSelection.Rd create mode 100644 man/stepwiseSettings.Rd create mode 100644 man/univariateSettings.Rd diff --git a/man/createBorutaFeatureSelection.Rd b/man/borutaSettings.Rd similarity index 85% rename from man/createBorutaFeatureSelection.Rd rename to man/borutaSettings.Rd index f23836e91..488a4f66a 100644 --- a/man/createBorutaFeatureSelection.Rd +++ b/man/borutaSettings.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Boruta.R -\name{createBorutaFeatureSelection} -\alias{createBorutaFeatureSelection} +% Please edit documentation in R/VariableSelection.R +\name{borutaSettings} +\alias{borutaSettings} \title{Create the settings for Boruta feature selection} \usage{ -createBorutaFeatureSelection( +borutaSettings( nJobs = -1L, maxDepth = 5L, nTrees = "auto", diff --git a/man/createRandomForestFeatureSelection.Rd b/man/createRandomForestFeatureSelection.Rd index 02eb82377..78977aa4b 100644 --- a/man/createRandomForestFeatureSelection.Rd +++ b/man/createRandomForestFeatureSelection.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/FeatureEngineering.R \name{createRandomForestFeatureSelection} \alias{createRandomForestFeatureSelection} -\title{Create the settings for random foreat based feature selection} +\title{Create the settings for defining any feature selection that will be done} \usage{ createRandomForestFeatureSelection(ntrees = 2000, maxDepth = 17) } @@ -10,13 +10,20 @@ createRandomForestFeatureSelection(ntrees = 2000, maxDepth = 17) \item{ntrees}{number of tree in forest} \item{maxDepth}{MAx depth of each tree} + +\item{k}{This function returns the K features most associated (univariately) to the outcome} } \value{ +An object of class \code{featureEngineeringSettings} +Create the settings for random foreat based feature selection + An object of class \code{featureEngineeringSettings} } \description{ -Create the settings for random foreat based feature selection +Create the settings for defining any feature selection that will be done } \details{ +Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings + Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings } diff --git a/man/createUnivariateFeatureSelection.Rd b/man/createUnivariateFeatureSelection.Rd deleted file mode 100644 index 3be0dcc83..000000000 --- a/man/createUnivariateFeatureSelection.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/FeatureEngineering.R -\name{createUnivariateFeatureSelection} -\alias{createUnivariateFeatureSelection} -\title{Create the settings for defining any feature selection that will be done} -\usage{ -createUnivariateFeatureSelection(k = 100) -} -\arguments{ -\item{k}{This function returns the K features most associated (univariately) to the outcome} -} -\value{ -An object of class \code{featureEngineeringSettings} -} -\description{ -Create the settings for defining any feature selection that will be done -} -\details{ -Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings -} diff --git a/man/doCyclopsCVPenalty.Rd b/man/doCyclopsCVPenalty.Rd new file mode 100644 index 000000000..35483ff9b --- /dev/null +++ b/man/doCyclopsCVPenalty.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/CyclopsModels.R +\name{doCyclopsCVPenalty} +\alias{doCyclopsCVPenalty} +\title{do simple CV to determine best penalty manually} +\usage{ +doCyclopsCVPenalty( + data, + prior, + fixedCoefficients, + startingCoefficients, + penaltyRatio = 0.1, + nTries = 10 +) +} +\arguments{ +\item{data}{The training data} + +\item{prior}{Cyclops prior to use} + +\item{fixedCoefficients}{What coefficients (if any) should be fixed} + +\item{startingCoefficients}{What coefficients (if any) should have some starting value} + +\item{penaltyRatio}{This controls the lowest penalty to try as a ratio of `BIC` penalty. +Trying very low penalties will increase computation times a lot.} + +\item{nTries}{How many penalties to try. Default: 10} +} +\description{ +do simple CV to determine best penalty manually +} +\details{ +Will try a sequence of penalties from `BIC` down to `penaltyRatio` * `BIC`. How many penalties +to try is determined by `nTries.` Will use cross-validation to determine optimal penalty +based on `AUC`. +} diff --git a/man/loadTrainTestData.Rd b/man/loadTrainTestData.Rd new file mode 100644 index 000000000..092f9ee30 --- /dev/null +++ b/man/loadTrainTestData.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/SaveLoadData.R +\name{loadTrainTestData} +\alias{loadTrainTestData} +\title{Load the cohort data from a folder} +\usage{ +loadTrainTestData(file, readOnly = TRUE) +} +\arguments{ +\item{file}{The name of the folder containing the data.} + +\item{readOnly}{If true, the data is opened read only.} +} +\value{ +Data object before runModelDevelopment. +} +\description{ +\code{loadTrainTestData} loads an data object before runModelDevelopment from a folder in the file. +system. +} +\details{ +The data will be written to a set of files in the folder specified by the user. +} +\examples{ +# todo + +} diff --git a/man/njmimSettings.Rd b/man/njmimSettings.Rd index 920271039..8458936ed 100644 --- a/man/njmimSettings.Rd +++ b/man/njmimSettings.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/NJMIM.R +% Please edit documentation in R/VariableSelection.R \name{njmimSettings} \alias{njmimSettings} \title{Create settings using normalized joint mutual information maximization (NJMIM) for feature selection} @@ -7,7 +7,7 @@ njmimSettings(k = 20) } \arguments{ -\item{k}{number of features to select} +\item{k}{number of variables to select, default `20`} } \description{ Create settings using normalized joint mutual information maximization (NJMIM) for feature selection diff --git a/man/saveTrainTestData.Rd b/man/saveTrainTestData.Rd new file mode 100644 index 000000000..79245cb9b --- /dev/null +++ b/man/saveTrainTestData.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/SaveLoadData.R +\name{saveTrainTestData} +\alias{saveTrainTestData} +\title{Save the cohort data to folder} +\usage{ +saveTrainTestData(data, file, test = T, envir = NULL, overwrite = F) +} +\arguments{ +\item{data}{Data object before runModelDevelopment.} + +\item{file}{The name of the folder where the data will be written. The folder should +not yet exist.} + +\item{envir}{The environment for to evaluate variables when saving.} + +\item{overwrite}{Whether to force overwrite an existing file} +} +\description{ +\code{saveTrainTestData} saves an data object before runModelDevelopment to folder. +} +\details{ +The data will be written to a set of files in the folder specified by the user. +} +\examples{ +# todo + +} diff --git a/man/setStepwiseSelection.Rd b/man/setStepwiseSelection.Rd deleted file mode 100644 index db6cb3666..000000000 --- a/man/setStepwiseSelection.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/VariableSelection.R -\name{setStepwiseSelection} -\alias{setStepwiseSelection} -\title{Selects features using stepwise selection} -\usage{ -setStepwiseSelection( - modelSettings, - selectMethod = "backward", - nInitialVariables = 50, - nVariables = 20, - stepSize = 1 -) -} -\arguments{ -\item{modelSettings}{settings of model to use in fit after selecting variables} - -\item{selectMethod}{`backward` or `forward` selection} - -\item{nInitialVariables}{# of variables to select initially} - -\item{nVariables}{amount of variables to select, default `20`} - -\item{stepSize}{How many variables to add/remove in each step} -} -\description{ -Selects features using stepwise selection -} diff --git a/man/setUnivariateSelection.Rd b/man/setUnivariateSelection.Rd deleted file mode 100644 index 7dececa6f..000000000 --- a/man/setUnivariateSelection.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/VariableSelection.R -\name{setUnivariateSelection} -\alias{setUnivariateSelection} -\title{Selects features based on univariate statistics} -\usage{ -setUnivariateSelection( - modelSettings = PatientLevelPrediction::setLassoLogisticRegression(), - corMethod = "pearson", - nVariables = 50 -) -} -\arguments{ -\item{modelSettings}{settings of model to use in fit after selecting variables} - -\item{corMethod}{which type of correlation to use, `pearson`, `kendall` or `spearman`. default `pearson`} - -\item{nVariables}{amount of variables to select, default `50`} -} -\description{ -Selects features based on univariate statistics -} diff --git a/man/stepwiseSettings.Rd b/man/stepwiseSettings.Rd new file mode 100644 index 000000000..c6faaa3eb --- /dev/null +++ b/man/stepwiseSettings.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/VariableSelection.R +\name{stepwiseSettings} +\alias{stepwiseSettings} +\title{Create settings using stepwise selection for feature selection} +\usage{ +stepwiseSettings( + k = 20, + selectMethod = "backward", + kStart = 100, + stepSize = 1, + modelSettings = PatientLevelPrediction::setLassoLogisticRegression() +) +} +\arguments{ +\item{k}{number of variables to select, default `20`} + +\item{selectMethod}{`backward` or `forward` selection} + +\item{kStart}{number of variables to select initially} + +\item{stepSize}{How many variables to add/remove in each step} + +\item{modelSettings}{settings of model to use in fit after selecting variables} +} +\description{ +Create settings using stepwise selection for feature selection +} diff --git a/man/univariateSettings.Rd b/man/univariateSettings.Rd new file mode 100644 index 000000000..d6bd1fd45 --- /dev/null +++ b/man/univariateSettings.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/VariableSelection.R +\name{univariateSettings} +\alias{univariateSettings} +\title{Create settings using univariate statistics for feature selection} +\usage{ +univariateSettings(k = 20, corMethod = "pearson") +} +\arguments{ +\item{k}{number of variables to select, default `20`} + +\item{corMethod}{which type of correlation to use, `pearson`, `kendall` or `spearman`. default `pearson`} +} +\description{ +Create settings using univariate statistics for feature selection +} From b6a06931d7217a041b32d87fbd41569d731c2d21 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Tue, 31 Oct 2023 15:11:43 +0100 Subject: [PATCH 21/28] Consistency names --- R/VariableSelection.R | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/R/VariableSelection.R b/R/VariableSelection.R index 0956b0086..3f7d99bfa 100644 --- a/R/VariableSelection.R +++ b/R/VariableSelection.R @@ -452,15 +452,12 @@ borutaFeatureSelection <- function( ){ if(is.null(covariateIdsInclude)){ - #convert data into matrix: - mappedData <- toSparseM(trainData) - - matrixData <- mappedData$dataMatrix - labels <- mappedData$labels - covariateMap <- mappedData$covariateMap - - X <- reticulate::r_to_py(matrixData) - y <- reticulate::r_to_py(matrix(labels$outcomeCount, ncol=1)) + sparseData <- toSparseM(trainData) + dataMatrix <- sparseData$dataMatrix + covariateMap <- sparseData$covariateMap + + X <- reticulate::r_to_py(dataMatrix) + y <- reticulate::r_to_py(matrix(sparseData$labels$outcomeCount, ncol=1)) sklearn <- reticulate::import('sklearn') BorutaPy <- reticulate::import('boruta')$BorutaPy From f36920ce45c0066f0efbf6a20e7f0ee798a03fc8 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Thu, 2 Nov 2023 10:32:31 +0100 Subject: [PATCH 22/28] Correct tests feature engineering + importance --- tests/testthat/test-featureEngineering.R | 6 +++--- tests/testthat/test-featureImportance.R | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/testthat/test-featureEngineering.R b/tests/testthat/test-featureEngineering.R index 04182d4d0..8bfc2d1f0 100644 --- a/tests/testthat/test-featureEngineering.R +++ b/tests/testthat/test-featureEngineering.R @@ -40,14 +40,14 @@ test_that("createFeatureEngineeringSettings correct class", { testUniFun <- function(k = 100){ - result <- createUnivariateFeatureSelection(k = k) + result <- univariateSettings(k = k) return(result) } -test_that("createUnivariateFeatureSelection correct class", { +test_that("univariateSettings correct class", { k <- sample(1000,1) featureEngineeringSettings <- testUniFun(k = k) @@ -176,7 +176,7 @@ test_that("featureSelection is applied on test_data", { } }) -test_that("njmim settings function works", { +test_that("NJMIM settings function works", { k <- sample(1000,1) featureEngineeringSettings <- njmimSettings(k = k) diff --git a/tests/testthat/test-featureImportance.R b/tests/testthat/test-featureImportance.R index 193f43b41..c6cb387c1 100644 --- a/tests/testthat/test-featureImportance.R +++ b/tests/testthat/test-featureImportance.R @@ -19,7 +19,7 @@ context("FeatureImportance") # Test unit for the creation of the study externalValidatePlp -test_that("pfi feature importance returns data.frame", { +test_that("feature importance returns data.frame", { # limit to a sample of 10 covariates for faster test covariates <- plpResult$model$covariateImportance %>% @@ -42,12 +42,12 @@ test_that("pfi feature importance returns data.frame", { }) -test_that('pfi feature importance works with logger or without covariates', { +test_that('feature importance works with logger or without covariates', { tinyResults <- runPlp(plpData = tinyPlpData, populationSettings = populationSettings, outcomeId = 2, analysisId = 'tinyFit', - featureEngineeringSettings = createUnivariateFeatureSelection(k=20), + featureEngineeringSettings = univariateSettings(k=20), executeSettings = createExecuteSettings( runSplitData = T, runSampleData = F, From 7c385c8d9e1fab644fe21335b18766a2c7ac175c Mon Sep 17 00:00:00 2001 From: egillax Date: Tue, 7 Nov 2023 11:33:42 +0100 Subject: [PATCH 23/28] handle case k < #covariates --- R/VariableSelection.R | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/R/VariableSelection.R b/R/VariableSelection.R index 3f7d99bfa..d8babde7b 100644 --- a/R/VariableSelection.R +++ b/R/VariableSelection.R @@ -129,6 +129,10 @@ univariateFeatureSelection <- function(trainData, dplyr::filter(columnId %in% selected) %>% dplyr::pull(covariateId) + } else { + # return all covariates + covariateIdsInclude <- sparseData$covariateMap %>% + dplyr::pull(covariateId) } } From 3ee55bd6ec6a21f558d84408cd855610da2dab78 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Thu, 9 Nov 2023 14:20:54 +0100 Subject: [PATCH 24/28] Correction documentation --- R/FeatureEngineering.R | 92 +---------------------- man/createRandomForestFeatureSelection.Rd | 15 +--- man/loadTrainTestData.Rd | 27 ------- man/saveTrainTestData.Rd | 28 ------- 4 files changed, 6 insertions(+), 156 deletions(-) delete mode 100644 man/loadTrainTestData.Rd delete mode 100644 man/saveTrainTestData.Rd diff --git a/R/FeatureEngineering.R b/R/FeatureEngineering.R index 0f40d38b1..486616e92 100644 --- a/R/FeatureEngineering.R +++ b/R/FeatureEngineering.R @@ -41,40 +41,13 @@ createFeatureEngineeringSettings <- function(type = 'none'){ } -#' Create the settings for defining any feature selection that will be done -#' -#' @details -#' Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings -#' -#' @param k This function returns the K features most associated (univariately) to the outcome -#' -#' @return -#' An object of class \code{featureEngineeringSettings} -# createUnivariateFeatureSelection <- function(k = 100){ -# -# if (inherits(k, 'numeric')) { -# k <- as.integer(k) -# } -# -# checkIsClass(k, 'integer') -# checkHigherEqual(k, 0) -# -# featureEngineeringSettings <- list(k = k) -# -# attr(featureEngineeringSettings, "fun") <- "univariateFeatureSelection" -# class(featureEngineeringSettings) <- "featureEngineeringSettings" -# -# return(featureEngineeringSettings) -# -# } - #' Create the settings for random foreat based feature selection #' #' @details #' Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings #' -#' @param ntrees number of tree in forest -#' @param maxDepth MAx depth of each tree +#' @param ntrees Number of tree in forest +#' @param maxDepth Max depth of each tree #' #' @return #' An object of class \code{featureEngineeringSettings} @@ -97,67 +70,6 @@ createRandomForestFeatureSelection <- function(ntrees = 2000, maxDepth = 17){ return(featureEngineeringSettings) } -# univariateFeatureSelection <- function( -# trainData, -# featureEngineeringSettings, -# covariateIdsInclude = NULL){ -# -# if(is.null(covariateIdsInclude)){ -# #convert data into matrix: -# mappedData <- toSparseM(trainData, trainData$labels) -# -# matrixData <- mappedData$dataMatrix -# labels <- mappedData$labels -# covariateMap <- mappedData$covariateMap -# -# X <- reticulate::r_to_py(matrixData) -# y <- reticulate::r_to_py(labels[,'outcomeCount']) -# -# np <- reticulate::import('numpy') -# os <- reticulate::import('os') -# sys <- reticulate::import('sys') -# math <- reticulate::import('math') -# scipy <- reticulate::import('scipy') -# -# sklearn <- reticulate::import('sklearn') -# -# SelectKBest <- sklearn$feature_selection$SelectKBest -# chi2 <- sklearn$feature_selection$chi2 -# -# kbest <- SelectKBest(chi2, k = featureEngineeringSettings$k)$fit(X, y$outcomeCount) -# kbest$scores_ <- np$nan_to_num(kbest$scores_) -# -# # taken from sklearn code, matches the application during transform call -# k <- featureEngineeringSettings$k -# mask <- np$zeros(length(kbest$scores_), dtype='bool') -# mask[np$argsort(kbest$scores_, kind="mergesort")+1][(length(kbest$scores_)-k+1):length(kbest$scores_)] <- TRUE -# -# covariateIdsInclude <- covariateMap[mask,]$covariateId -# } -# -# trainData$covariateData$covariates <- trainData$covariateData$covariates %>% -# dplyr::filter(.data$covariateId %in% covariateIdsInclude) -# -# trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% -# dplyr::filter(.data$covariateId %in% covariateIdsInclude) -# -# featureEngineering <- list( -# funct = 'univariateFeatureSelection', -# settings = list( -# featureEngineeringSettings = featureEngineeringSettings, -# covariateIdsInclude = covariateIdsInclude -# ) -# ) -# -# attr(trainData, 'metaData')$featureEngineering = listAppend( -# attr(trainData, 'metaData')$featureEngineering, -# featureEngineering -# ) -# -# return(trainData) -# -# } - randomForestFeatureSelection <- function( trainData, diff --git a/man/createRandomForestFeatureSelection.Rd b/man/createRandomForestFeatureSelection.Rd index 78977aa4b..ca7157573 100644 --- a/man/createRandomForestFeatureSelection.Rd +++ b/man/createRandomForestFeatureSelection.Rd @@ -2,28 +2,21 @@ % Please edit documentation in R/FeatureEngineering.R \name{createRandomForestFeatureSelection} \alias{createRandomForestFeatureSelection} -\title{Create the settings for defining any feature selection that will be done} +\title{Create the settings for random foreat based feature selection} \usage{ createRandomForestFeatureSelection(ntrees = 2000, maxDepth = 17) } \arguments{ -\item{ntrees}{number of tree in forest} +\item{ntrees}{Number of tree in forest} -\item{maxDepth}{MAx depth of each tree} - -\item{k}{This function returns the K features most associated (univariately) to the outcome} +\item{maxDepth}{Max depth of each tree} } \value{ -An object of class \code{featureEngineeringSettings} -Create the settings for random foreat based feature selection - An object of class \code{featureEngineeringSettings} } \description{ -Create the settings for defining any feature selection that will be done +Create the settings for random foreat based feature selection } \details{ -Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings - Returns an object of class \code{featureEngineeringSettings} that specifies the sampling function that will be called and the settings } diff --git a/man/loadTrainTestData.Rd b/man/loadTrainTestData.Rd deleted file mode 100644 index 092f9ee30..000000000 --- a/man/loadTrainTestData.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/SaveLoadData.R -\name{loadTrainTestData} -\alias{loadTrainTestData} -\title{Load the cohort data from a folder} -\usage{ -loadTrainTestData(file, readOnly = TRUE) -} -\arguments{ -\item{file}{The name of the folder containing the data.} - -\item{readOnly}{If true, the data is opened read only.} -} -\value{ -Data object before runModelDevelopment. -} -\description{ -\code{loadTrainTestData} loads an data object before runModelDevelopment from a folder in the file. -system. -} -\details{ -The data will be written to a set of files in the folder specified by the user. -} -\examples{ -# todo - -} diff --git a/man/saveTrainTestData.Rd b/man/saveTrainTestData.Rd deleted file mode 100644 index 79245cb9b..000000000 --- a/man/saveTrainTestData.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/SaveLoadData.R -\name{saveTrainTestData} -\alias{saveTrainTestData} -\title{Save the cohort data to folder} -\usage{ -saveTrainTestData(data, file, test = T, envir = NULL, overwrite = F) -} -\arguments{ -\item{data}{Data object before runModelDevelopment.} - -\item{file}{The name of the folder where the data will be written. The folder should -not yet exist.} - -\item{envir}{The environment for to evaluate variables when saving.} - -\item{overwrite}{Whether to force overwrite an existing file} -} -\description{ -\code{saveTrainTestData} saves an data object before runModelDevelopment to folder. -} -\details{ -The data will be written to a set of files in the folder specified by the user. -} -\examples{ -# todo - -} From 749c97a9e86a7dc367eec40c0820d281a0421a54 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Fri, 10 Nov 2023 14:48:33 +0100 Subject: [PATCH 25/28] Rename param covariateIdsInclude to covariateIdsSelected for new methods --- R/FeatureEngineering.R | 2 +- R/VariableSelection.R | 50 ++++++++++++------------ tests/testthat/test-featureEngineering.R | 9 +++-- 3 files changed, 31 insertions(+), 30 deletions(-) diff --git a/R/FeatureEngineering.R b/R/FeatureEngineering.R index 486616e92..9d6a6dd81 100644 --- a/R/FeatureEngineering.R +++ b/R/FeatureEngineering.R @@ -74,7 +74,7 @@ createRandomForestFeatureSelection <- function(ntrees = 2000, maxDepth = 17){ randomForestFeatureSelection <- function( trainData, featureEngineeringSettings, - covariateIdsInclude = NULL + covariateIdsInclude = NULL # equivalent to covariateIdsSelected in VariableSelection.R ){ if(is.null(covariateIdsInclude)){ diff --git a/R/VariableSelection.R b/R/VariableSelection.R index d8babde7b..b59f86b23 100644 --- a/R/VariableSelection.R +++ b/R/VariableSelection.R @@ -37,8 +37,8 @@ njmimSettings <- function(k = 20) { njmimFeatureSelection <- function(trainData, featureEngineeringSettings, - covariateIdsInclude = NULL) { - if (is.null(covariateIdsInclude)) { + covariateIdsSelected = NULL) { + if (is.null(covariateIdsSelected)) { sparseData <- toSparseM(trainData, trainData$labels) denseMatrix <- as.matrix(sparseData$dataMatrix) dataFrame <- as.data.frame(denseMatrix) @@ -48,21 +48,21 @@ njmimFeatureSelection <- function(trainData, Y = y, k = featureEngineeringSettings$k) - covariateIdsInclude <- sparseData$covariateMap %>% + covariateIdsSelected <- sparseData$covariateMap %>% dplyr::filter(columnId %in% selection$selection) %>% dplyr::pull(covariateId) } trainData$covariateData$covariates <- trainData$covariateData$covariates %>% - dplyr::filter(covariateId %in% covariateIdsInclude) + dplyr::filter(covariateId %in% covariateIdsSelected) trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% - dplyr::filter(covariateId %in% covariateIdsInclude) + dplyr::filter(covariateId %in% covariateIdsSelected) featureEngineering <- list( funct = 'njmimFeatureSelection', settings = list( featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = covariateIdsInclude + covariateIdsSelected = covariateIdsSelected ) ) @@ -105,8 +105,8 @@ univariateSettings <- function(k = 20, # TODO: set dynamic number based on elbow univariateFeatureSelection <- function(trainData, featureEngineeringSettings, - covariateIdsInclude = NULL) { - if (is.null(covariateIdsInclude)) { + covariateIdsSelected = NULL) { + if (is.null(covariateIdsSelected)) { sparseData <- toSparseM(trainData, trainData$labels) denseMatrix <- as.matrix(sparseData$dataMatrix) dataFrame <- as.data.frame(denseMatrix) @@ -125,27 +125,27 @@ univariateFeatureSelection <- function(trainData, # Select variables selected <- names(correlation)[1:min(featureEngineeringSettings$k, length(correlation))] - covariateIdsInclude <- sparseData$covariateMap %>% + covariateIdsSelected <- sparseData$covariateMap %>% dplyr::filter(columnId %in% selected) %>% dplyr::pull(covariateId) } else { # return all covariates - covariateIdsInclude <- sparseData$covariateMap %>% + covariateIdsSelected <- sparseData$covariateMap %>% dplyr::pull(covariateId) } } trainData$covariateData$covariates <- trainData$covariateData$covariates %>% - dplyr::filter(covariateId %in% covariateIdsInclude) + dplyr::filter(covariateId %in% covariateIdsSelected) trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% - dplyr::filter(covariateId %in% covariateIdsInclude) + dplyr::filter(covariateId %in% covariateIdsSelected) featureEngineering <- list( funct = 'univariateFeatureSelection', settings = list( featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = covariateIdsInclude + covariateIdsSelected = covariateIdsSelected ) ) @@ -200,8 +200,8 @@ stepwiseSettings <- function(k = 20, # TODO: set dynamic number based on elbow stepwiseFeatureSelection <- function(trainData, featureEngineeringSettings, - covariateIdsInclude = NULL) { - if (is.null(covariateIdsInclude)) { + covariateIdsSelected = NULL) { + if (is.null(covariateIdsSelected)) { search <- 999 # TODO: which number to use? analysisId <- 999 # TODO: which number to use? @@ -250,19 +250,19 @@ stepwiseFeatureSelection <- function(trainData, } covariates <- as.data.frame(update_trainData$covariateData$covariates) - covariateIdsInclude <- unique(covariates$covariateId) + covariateIdsSelected <- unique(covariates$covariateId) } trainData$covariateData$covariates <- trainData$covariateData$covariates %>% - dplyr::filter(covariateId %in% covariateIdsInclude) + dplyr::filter(covariateId %in% covariateIdsSelected) trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% - dplyr::filter(covariateId %in% covariateIdsInclude) + dplyr::filter(covariateId %in% covariateIdsSelected) featureEngineering <- list( funct = 'univariateFeatureSelection', settings = list( featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = covariateIdsInclude + covariateIdsSelected = covariateIdsSelected ) ) @@ -452,10 +452,10 @@ borutaSettings <- function(nJobs = -1L, borutaFeatureSelection <- function( trainData, featureEngineeringSettings, - covariateIdsInclude = NULL + covariateIdsSelected = NULL ){ - if(is.null(covariateIdsInclude)){ + if(is.null(covariateIdsSelected)){ sparseData <- toSparseM(trainData) dataMatrix <- sparseData$dataMatrix covariateMap <- sparseData$covariateMap @@ -481,24 +481,24 @@ borutaFeatureSelection <- function( includedFeatures <- featureSelector$support_ - covariateIdsInclude <- covariateMap %>% + covariateIdsSelected <- covariateMap %>% dplyr::filter(.data$columnId %in% which(includedFeatures)) %>% dplyr::select("covariateId") %>% dplyr::arrange("covariateId") %>% dplyr::pull() } trainData$covariateData$covariates <- trainData$covariateData$covariates %>% - dplyr::filter(.data$covariateId %in% covariateIdsInclude) + dplyr::filter(.data$covariateId %in% covariateIdsSelected) trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% - dplyr::filter(.data$covariateId %in% covariateIdsInclude) + dplyr::filter(.data$covariateId %in% covariateIdsSelected) featureEngineering <- list( funct = 'borutaFeatureSelection', settings = list( featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = covariateIdsInclude + covariateIdsSelected = covariateIdsSelected ) ) diff --git a/tests/testthat/test-featureEngineering.R b/tests/testthat/test-featureEngineering.R index 8bfc2d1f0..fbb9f1e36 100644 --- a/tests/testthat/test-featureEngineering.R +++ b/tests/testthat/test-featureEngineering.R @@ -72,7 +72,7 @@ test_that("univariateFeatureSelection", { reducedTrainData <- univariateFeatureSelection( trainData = newTrainData, featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = NULL + covariateIdsSelected = NULL ) newDataCovariateSize <- reducedTrainData$covariateData$covariates %>% dplyr::tally() %>% dplyr::pull() @@ -144,7 +144,8 @@ test_that("randomForestFeatureSelection", { reducedTrainData <- randomForestFeatureSelection( trainData = newTrainData, featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = NULL + covariateIdsSelected = NULL + ) newDataCovariateSize <- reducedTrainData$covariateData$covariates %>% dplyr::tally() %>% dplyr::pull() @@ -159,7 +160,7 @@ test_that("featureSelection is applied on test_data", { newTrainData <- univariateFeatureSelection( trainData = newTrainData, featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = NULL + covariateIdsSelected = NULL ) modelSettings <- setLassoLogisticRegression() @@ -200,7 +201,7 @@ test_that("NJMIM feature selection works", { reducedTrainData <- njmimFeatureSelection( trainData = newTrainData, featureEngineeringSettings = featureEngineeringSettings, - covariateIdsInclude = NULL + covariateIdsSelected = NULL ) newDataCovariateSize <- reducedTrainData$covariateData$covariates %>% dplyr::tally() %>% dplyr::pull() From de8dff9cdf8ab29219fd725c5b6f44ab85c501ff Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Tue, 14 Nov 2023 13:48:23 +0100 Subject: [PATCH 26/28] Add check for K in NJMIM --- R/VariableSelection.R | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/R/VariableSelection.R b/R/VariableSelection.R index b59f86b23..7530831a8 100644 --- a/R/VariableSelection.R +++ b/R/VariableSelection.R @@ -44,13 +44,20 @@ njmimFeatureSelection <- function(trainData, dataFrame <- as.data.frame(denseMatrix) y <- sparseData$labels$outcomeCount - selection <- praznik::NJMIM(X = dataFrame, - Y = y, - k = featureEngineeringSettings$k) + if (ncol(dataFrame) > featureEngineeringSettings$k) { + selection <- praznik::NJMIM(X = dataFrame, + Y = y, + k = featureEngineeringSettings$k) + + covariateIdsSelected <- sparseData$covariateMap %>% + dplyr::filter(columnId %in% selection$selection) %>% + dplyr::pull(covariateId) + } else { + # return all covariates + covariateIdsSelected <- sparseData$covariateMap %>% + dplyr::pull(covariateId) + } - covariateIdsSelected <- sparseData$covariateMap %>% - dplyr::filter(columnId %in% selection$selection) %>% - dplyr::pull(covariateId) } trainData$covariateData$covariates <- trainData$covariateData$covariates %>% @@ -459,7 +466,7 @@ borutaFeatureSelection <- function( sparseData <- toSparseM(trainData) dataMatrix <- sparseData$dataMatrix covariateMap <- sparseData$covariateMap - + X <- reticulate::r_to_py(dataMatrix) y <- reticulate::r_to_py(matrix(sparseData$labels$outcomeCount, ncol=1)) @@ -480,7 +487,6 @@ borutaFeatureSelection <- function( includedFeatures <- featureSelector$support_ - covariateIdsSelected <- covariateMap %>% dplyr::filter(.data$columnId %in% which(includedFeatures)) %>% dplyr::select("covariateId") %>% dplyr::arrange("covariateId") %>% @@ -493,7 +499,6 @@ borutaFeatureSelection <- function( trainData$covariateData$covariateRef <- trainData$covariateData$covariateRef %>% dplyr::filter(.data$covariateId %in% covariateIdsSelected) - featureEngineering <- list( funct = 'borutaFeatureSelection', settings = list( From e52c65f36c8f3f1a0d67c94d74dfa6070d0dd3d9 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Mon, 18 Dec 2023 14:28:10 +0100 Subject: [PATCH 27/28] Update shiny config to match OhdsiShinyModules --- inst/shinyConfig.json | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/inst/shinyConfig.json b/inst/shinyConfig.json index f2a674932..6034907d4 100644 --- a/inst/shinyConfig.json +++ b/inst/shinyConfig.json @@ -7,11 +7,8 @@ "shinyModulePackage": "OhdsiShinyModules", "uiFunction": "aboutViewer", "serverFunction": "aboutServer", - "databaseConnectionKeyService": null, - "databaseConnectionKeyUsername": null, "infoBoxFile": "aboutHelperFile()", "icon": "info", - "keyring": true, "order": 1 }, { @@ -19,14 +16,11 @@ "tabName": "Prediction", "tabText": "Prediction", "shinyModulePackage": "OhdsiShinyModules", - "uiFunction": "predictionViewer", - "serverFunction": "predictionServer", - "databaseConnectionKeyService": "resultDatabaseDetails", - "databaseConnectionKeyUsername": "prediction", - "infoBoxFile": "predictionHelperFile()", + "uiFunction": "patientLevelPredictionViewer", + "serverFunction": "patientLevelPredictionServer", + "infoBoxFile": "patientLevelPredictionHelperFile()", "icon": "chart-line", - "keyring": false, "order": 2 } ] -} +} \ No newline at end of file From 76ac0f57c372d2298fa16e4eb4458b17e1f60199 Mon Sep 17 00:00:00 2001 From: aniekmarkus Date: Mon, 18 Dec 2023 14:46:44 +0100 Subject: [PATCH 28/28] Update to fix shiny viewer --- R/ViewShinyPlp.R | 68 ++++++++++++++++++++++++++++++++++-------------- 1 file changed, 49 insertions(+), 19 deletions(-) diff --git a/R/ViewShinyPlp.R b/R/ViewShinyPlp.R index f80c22f1e..745f0f57a 100644 --- a/R/ViewShinyPlp.R +++ b/R/ViewShinyPlp.R @@ -50,7 +50,7 @@ viewPlp <- function(runPlp, validatePlp = NULL, diagnosePlp = NULL) { runPlp = runPlp, externalValidatePlp = validatePlp, diagnosePlp = diagnosePlp - ) + ) connectionDetailSettings <- list( dbms = 'sqlite', @@ -69,7 +69,7 @@ viewPlp <- function(runPlp, validatePlp = NULL, diagnosePlp = NULL) { ) viewPlps(databaseSettings) - + } @@ -88,14 +88,14 @@ viewPlp <- function(runPlp, validatePlp = NULL, diagnosePlp = NULL) { #' #' @export viewDatabaseResultPlp <- function( - mySchema, - myServer, - myUser, - myPassword, - myDbms, - myPort = NULL, - myTableAppend - ){ + mySchema, + myServer, + myUser, + myPassword, + myDbms, + myPort = NULL, + myTableAppend +){ connectionDetailSettings <- list( dbms = myDbms, @@ -132,19 +132,49 @@ viewPlps <- function(databaseSettings){ connectionDetails <- do.call( DatabaseConnector::createConnectionDetails, databaseSettings$connectionDetailSettings - ) + ) connection <- ResultModelManager::ConnectionHandler$new(connectionDetails) databaseSettings$connectionDetailSettings <- NULL - # set database settings into system variables - Sys.setenv("resultDatabaseDetails_prediction" = as.character(ParallelLogger::convertSettingsToJson(databaseSettings))) - - config <- ParallelLogger::loadSettingsFromJson( - fileName = system.file( - 'shinyConfig.json', - package = "PatientLevelPrediction" + shinyAppVersion <- strsplit(x = as.character(utils::packageVersion('ShinyAppBuilder')), split = '\\.')[[1]] + + if((shinyAppVersion[1] <= 1 & shinyAppVersion[2] < 2)){ + # Old code to be backwards compatable + config <- ParallelLogger::loadSettingsFromJson( + fileName = system.file( + 'shinyConfig.json', + package = "PatientLevelPrediction" + ) + ) + # set database settings into system variables + Sys.setenv("resultDatabaseDetails_prediction" = as.character(ParallelLogger::convertSettingsToJson(databaseSettings))) + ShinyAppBuilder::viewShiny( + config = config, + connection = connection ) + } else{ + ohdsiModulesVersion <- strsplit(x = as.character(utils::packageVersion('OhdsiShinyModules')), split = '\\.')[[1]] + if(paste0(ohdsiModulesVersion[1], ".", ohdsiModulesVersion[2])>= 1.2){ + config <- ParallelLogger::loadSettingsFromJson( + fileName = system.file( + 'shinyConfigUpdate.json', + package = "PatientLevelPrediction" + ) ) + databaseSettings$plpTablePrefix = databaseSettings$tablePrefix + databaseSettings$cgTablePrefix = databaseSettings$tablePrefix + databaseSettings$databaseTable = 'database_meta_table' + databaseSettings$databaseTablePrefix = databaseSettings$tablePrefix + ShinyAppBuilder::viewShiny( + config = config, + connection = connection, + resultDatabaseSettings = databaseSettings + ) + } else{ + ParallelLogger::logWarn('Need to update package OhdsiShinyModules') + } + + } + - ShinyAppBuilder::viewShiny(config = config, connection = connection) } \ No newline at end of file

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