# @file lassoLogisticRegression.R
#
# Copyright 2021 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.
fitCyclopsModel <- function(
trainData,
modelSettings, # old:param,
search='adaptive',
analysisId,
...){
param <- modelSettings$param
# check plpData is coo format:
if (!FeatureExtraction::isCovariateData(trainData$covariateData)){
stop("Needs correct covariateData")
}
settings <- attr(param, 'settings')
trainData$covariateData$labels <- trainData$labels %>%
dplyr::mutate(
y = sapply(.data$outcomeCount, function(x) min(1,x)),
time = .data$survivalTime
)
covariates <- filterCovariateIds(param, trainData$covariateData)
if (!is.null(param$priorCoefs)) {
sourceCoefs <- param$priorCoefs %>%
dplyr::filter(abs(.data$betas)>0 & .data$covariateIds != "(Intercept)")
newCovariates <- 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)
}
start <- Sys.time()
cyclopsData <- Cyclops::convertToCyclopsData(
outcomes = trainData$covariateData$labels,
covariates = covariates,
addIntercept = settings$addIntercept,
modelType = modelTypeToCyclopsModelType(settings$modelType),
checkRowIds = FALSE,
normalize = NULL,
quiet = TRUE
)
if (!is.null(param$priorCoefs)) {
fixedCoefficients <- c(FALSE,
rep(TRUE, nrow(sourceCoefs)),
rep(FALSE, length(cyclopsData$coefficientNames)-(nrow(sourceCoefs)+1)))
startingCoefficients <- rep(0, length(fixedCoefficients))
# skip intercept index
startingCoefficients[2:(nrow(sourceCoefs)+1)] <- sourceCoefs$betas
} else {
startingCoefficients <- NULL
fixedCoefficients <- NULL
}
if(settings$crossValidationInPrior){
param$priorParams$useCrossValidation <- max(trainData$folds$index)>1
}
prior <- do.call(eval(parse(text = settings$priorfunction)), param$priorParams)
if(settings$useControl){
control <- Cyclops::createControl(
cvType = "auto",
fold = max(trainData$folds$index),
startingVariance = param$priorParams$variance,
lowerLimit = param$lowerLimit,
upperLimit = param$upperLimit,
tolerance = settings$tolerance,
cvRepetitions = 1, # make an option?
selectorType = settings$selectorType,
noiseLevel = "silent",
threads = settings$threads,
maxIterations = settings$maxIterations,
seed = settings$seed
)
fit <- tryCatch({
ParallelLogger::logInfo('Running Cyclops')
Cyclops::fitCyclopsModel(
cyclopsData = cyclopsData,
prior = prior,
control = control,
fixedCoefficients = fixedCoefficients,
startingCoefficients = startingCoefficients
)},
finally = ParallelLogger::logInfo('Done.')
)
} else{
fit <- tryCatch({
ParallelLogger::logInfo('Running Cyclops with fixed varience')
Cyclops::fitCyclopsModel(cyclopsData, prior = prior)},
finally = ParallelLogger::logInfo('Done.'))
}
modelTrained <- createCyclopsModel(
fit = fit,
modelType = settings$modelType,
useCrossValidation = max(trainData$folds$index)>1,
cyclopsData = cyclopsData,
labels = trainData$covariateData$labels,
folds = trainData$folds,
priorType = param$priorParams$priorType
)
if (!is.null(param$priorCoefs)) {
modelTrained$coefficients <- reparamTransferCoefs(modelTrained$coefficients)
}
# TODO get optimal lambda value
ParallelLogger::logTrace('Returned from fitting to LassoLogisticRegression')
comp <- Sys.time() - start
ParallelLogger::logTrace('Getting variable importance')
variableImportance <- getVariableImportance(modelTrained, trainData)
#get prediction on test set:
ParallelLogger::logTrace('Getting predictions on train set')
tempModel <- list(model = modelTrained)
attr(tempModel, "modelType") <- attr(param, 'modelType')
prediction <- predictCyclops(
plpModel = tempModel,
cohort = trainData$labels,
data = trainData
)
prediction$evaluationType <- 'Train'
# get cv AUC if exists
cvPerFold <- data.frame()
if(!is.null(modelTrained$cv)){
cvPrediction <- do.call(rbind, lapply(modelTrained$cv, function(x){x$predCV}))
cvPrediction$evaluationType <- 'CV'
# fit date issue convertion caused by andromeda
cvPrediction$cohortStartDate <- as.Date(cvPrediction$cohortStartDate, origin = '1970-01-01')
prediction <- rbind(prediction, cvPrediction[,colnames(prediction)])
cvPerFold <- unlist(lapply(modelTrained$cv, function(x){x$out_sample_auc}))
if(length(cvPerFold)>0){
cvPerFold <- data.frame(
metric = 'AUC',
fold = 1:length(cvPerFold),
value = cvPerFold,
startingVariance = ifelse(is.null(param$priorParams$variance), 'NULL', param$priorParams$variance),
lowerLimit = ifelse(is.null(param$lowerLimit), 'NULL', param$lowerLimit),
upperLimit = ifelse(is.null(param$upperLimit), 'NULL', param$upperLimit),
tolerance = ifelse(is.null(settings$tolerance), 'NULL', settings$tolerance)
)
} else{
cvPerFold <- data.frame()
}
# remove the cv from the model:
modelTrained$cv <- NULL
}
result <- list(
model = modelTrained,
preprocessing = list(
featureEngineering = attr(trainData, "metaData")$featureEngineering,#learned mapping
tidyCovariates = attr(trainData$covariateData, "metaData")$tidyCovariateDataSettings, #learned mapping
requireDenseMatrix = F
),
prediction = prediction,
modelDesign = PatientLevelPrediction::createModelDesign(
targetId = attr(trainData, "metaData")$targetId, # added
outcomeId = attr(trainData, "metaData")$outcomeId, # added
restrictPlpDataSettings = attr(trainData, "metaData")$restrictPlpDataSettings, # made this restrictPlpDataSettings
covariateSettings = attr(trainData, "metaData")$covariateSettings,
populationSettings = attr(trainData, "metaData")$populationSettings,
featureEngineeringSettings = attr(trainData, "metaData")$featureEngineeringSettings,
preprocessSettings = attr(trainData$covariateData, "metaData")$preprocessSettings,
modelSettings = modelSettings, #modified
splitSettings = attr(trainData, "metaData")$splitSettings,
sampleSettings = attr(trainData, "metaData")$sampleSettings
),
trainDetails = list(
analysisId = analysisId,
analysisSource = '', #TODO add from model
developmentDatabase = attr(trainData, "metaData")$cdmDatabaseName,
developmentDatabaseSchema = attr(trainData, "metaData")$cdmDatabaseSchema,
attrition = attr(trainData, "metaData")$attrition,
trainingTime = paste(as.character(abs(comp)), attr(comp,'units')),
trainingDate = Sys.Date(),
modelName = settings$modelType,
finalModelParameters = list(
variance = modelTrained$priorVariance,
log_likelihood = modelTrained$log_likelihood
),
hyperParamSearch = cvPerFold
),
covariateImportance = variableImportance
)
class(result) <- 'plpModel'
attr(result, 'predictionFunction') <- 'predictCyclops'
attr(result, 'modelType') <- attr(param, 'modelType')
attr(result, 'saveType') <- attr(param, 'saveType')
return(result)
}
#' Create predictive probabilities
#'
#' @details
#' Generates predictions for the population specified in plpData given the model.
#'
#' @return
#' The value column in the result data.frame is: logistic: probabilities of the outcome, poisson:
#' Poisson rate (per day) of the outome, survival: hazard rate (per day) of the outcome.
#'
#' @param plpModel An object of type \code{predictiveModel} as generated using
#' \code{\link{fitPlp}}.
#' @param data The new plpData containing the covariateData for the new population
#' @param cohort The cohort to calculate the prediction for
#' @export
predictCyclops <- function(plpModel, data, cohort ) {
start <- Sys.time()
ParallelLogger::logTrace('predictProbabilities - predictAndromeda start')
prediction <- predictCyclopsType(
plpModel$model$coefficients,
cohort,
data$covariateData,
plpModel$model$modelType
)
# survival cyclops use baseline hazard to convert to risk from exp(LP) to 1-S^exp(LP)
if(attr(plpModel, 'modelType') == 'survival'){
if(!is.null(plpModel$model$baselineSurvival)){
if(is.null(attr(cohort, 'timepoint'))){
timepoint <- attr(cohort,'metaData')$populationSettings$riskWindowEnd
} else{
timepoint <- attr(cohort, 'timepoint')
}
bhind <- which.min(abs(plpModel$model$baselineSurvival$time-timepoint))
# 1- baseline survival(time)^ (exp(betas*values))
prediction$value <- 1-plpModel$model$baselineSurvival$surv[bhind]^prediction$value
metaData <- list()
metaData$baselineSurvivalTimepoint <- plpModel$model$baselineSurvival$time[bhind]
metaData$baselineSurvival <- plpModel$model$baselineSurvival$surv[bhind]
metaData$offset <- 0
attr(prediction, 'metaData') <- metaData
}
}
delta <- Sys.time() - start
ParallelLogger::logInfo("Prediction took ", signif(delta, 3), " ", attr(delta, "units"))
return(prediction)
}
predictCyclopsType <- function(coefficients, population, covariateData, modelType = "logistic") {
if (!(modelType %in% c("logistic", "poisson", "survival","cox"))) {
stop(paste("Unknown modelType:", modelType))
}
if (!FeatureExtraction::isCovariateData(covariateData)){
stop("Needs correct covariateData")
}
intercept <- coefficients$betas[coefficients$covariateId%in%'(Intercept)']
if(length(intercept)==0) intercept <- 0
betas <- coefficients$betas[!coefficients$covariateIds%in%'(Intercept)']
coefficients <- data.frame(beta = betas,
covariateId = coefficients$covariateIds[coefficients$covariateIds!='(Intercept)']
)
coefficients <- coefficients[coefficients$beta != 0, ]
if(sum(coefficients$beta != 0)>0){
covariateData$coefficients <- coefficients
on.exit(covariateData$coefficients <- NULL, add = TRUE)
prediction <- covariateData$covariates %>%
dplyr::inner_join(covariateData$coefficients, by= 'covariateId') %>%
dplyr::mutate(values = .data$covariateValue*.data$beta) %>%
dplyr::group_by(.data$rowId) %>%
dplyr::summarise(value = sum(.data$values, na.rm = TRUE)) %>%
dplyr::select("rowId", "value")
prediction <- as.data.frame(prediction)
prediction <- merge(population, prediction, by ="rowId", all.x = TRUE, fill = 0)
prediction$value[is.na(prediction$value)] <- 0
prediction$value <- prediction$value + intercept
} else{
warning('Model had no non-zero coefficients so predicted same for all population...')
prediction <- population
prediction$value <- rep(0, nrow(population)) + intercept
}
if (modelType == "logistic") {
link <- function(x) {
return(1/(1 + exp(0 - x)))
}
prediction$value <- link(prediction$value)
attr(prediction, "metaData")$modelType <- 'binary'
} else if (modelType == "poisson" || modelType == "survival" || modelType == "cox") {
# add baseline hazard stuff
prediction$value <- exp(prediction$value)
attr(prediction, "metaData")$modelType <- 'survival'
if(modelType == "survival"){ # is this needed?
attr(prediction, 'metaData')$timepoint <- max(population$survivalTime, na.rm = T)
}
}
return(prediction)
}
createCyclopsModel <- function(fit, modelType, useCrossValidation, cyclopsData, labels, folds,
priorType){
if (is.character(fit)) {
coefficients <- c(0)
names(coefficients) <- ''
status <- fit
} else if (fit$return_flag == "ILLCONDITIONED") {
coefficients <- c(0)
names(coefficients) <- ''
status <- "ILL CONDITIONED, CANNOT FIT"
ParallelLogger::logWarn(paste("GLM fitting issue: ", status))
} else if (fit$return_flag == "MAX_ITERATIONS") {
coefficients <- c(0)
names(coefficients) <- ''
status <- "REACHED MAXIMUM NUMBER OF ITERATIONS, CANNOT FIT"
ParallelLogger::logWarn(paste("GLM fitting issue: ", status))
} else {
status <- "OK"
coefficients <- stats::coef(fit) # not sure this is stats??
ParallelLogger::logInfo(paste("GLM fit status: ", status))
}
# use a dataframe for the coefficients
betas <- as.numeric(coefficients)
betaNames <- names(coefficients)
coefficients <- data.frame(betas=betas, covariateIds=betaNames)
outcomeModel <- list(
priorVariance = fit$variance,
log_likelihood = fit$log_likelihood,
modelType = modelType,
modelStatus = status,
coefficients = coefficients
)
if(modelType == "cox" || modelType == "survival") {
baselineSurvival <- tryCatch({survival::survfit(fit, type = "aalen")},
error = function(e) {ParallelLogger::logInfo(e); return(NULL)})
if(is.null(baselineSurvival)){
ParallelLogger::logInfo('No baseline hazard function returned')
}
outcomeModel$baselineSurvival <- baselineSurvival
}
class(outcomeModel) <- "plpModel"
#get CV - added && status == "OK" to only run if the model fit sucsessfully
if(modelType == "logistic" && useCrossValidation && status == "OK"){
outcomeModel$cv <- getCV(cyclopsData, labels, cvVariance = fit$variance, folds = folds,
priorType = priorType)
}
return(outcomeModel)
}
modelTypeToCyclopsModelType <- function(modelType, stratified=F) {
if (modelType == "logistic") {
if (stratified)
return("clr")
else
return("lr")
} else if (modelType == "poisson") {
if (stratified)
return("cpr")
else
return("pr")
} else if (modelType == "cox") {
return("cox")
} else {
ParallelLogger::logError(paste("Unknown model type:", modelType))
stop()
}
}
getCV <- function(
cyclopsData,
labels,
cvVariance,
folds,
priorType
)
{
fixed_prior <- Cyclops::createPrior(priorType = priorType,
variance = cvVariance,
useCrossValidation = FALSE)
# add the index to the labels
labels <- merge(labels, folds, by = 'rowId')
result <- lapply(1:max(labels$index), function(i) {
hold_out <- labels$index==i
weights <- rep(1.0, Cyclops::getNumberOfRows(cyclopsData))
weights[hold_out] <- 0.0
subset_fit <- suppressWarnings(Cyclops::fitCyclopsModel(cyclopsData,
prior = fixed_prior,
weights = weights))
predict <- stats::predict(subset_fit)
auc <- aucWithoutCi(predict[hold_out], labels$y[hold_out])
predCV <- cbind(labels[hold_out,],
value = predict[hold_out])
#predCV$outcomeCount <- predCV$y
return(list(out_sample_auc = auc,
predCV = predCV,
log_likelihood = subset_fit$log_likelihood,
log_prior = subset_fit$log_prior,
coef = stats::coef(subset_fit)))
})
return(result)
}
getVariableImportance <- function(modelTrained, trainData){
varImp <- data.frame(
covariateId = as.double(modelTrained$coefficients$covariateIds[modelTrained$coefficients$covariateIds!='(Intercept)']),
value = modelTrained$coefficients$betas[modelTrained$coefficients$covariateIds!='(Intercept)']
)
if(sum(abs(varImp$value)>0)==0){
ParallelLogger::logWarn('No non-zero coefficients')
varImp <- NULL
} else {
ParallelLogger::logInfo('Creating variable importance data frame')
#trainData$covariateData$varImp <- varImp
#on.exit(trainData$covariateData$varImp <- NULL, add = T)
varImp <- trainData$covariateData$covariateRef %>%
dplyr::collect() %>%
#dplyr::left_join(trainData$covariateData$varImp) %>%
dplyr::left_join(varImp, by = 'covariateId') %>%
dplyr::mutate(covariateValue = ifelse(is.na(.data$value), 0, .data$value)) %>%
dplyr::select(-"value") %>%
dplyr::arrange(-abs(.data$covariateValue)) %>%
dplyr::collect()
}
return(varImp)
}
filterCovariateIds <- function(param, covariateData){
if ( (length(param$includeCovariateIds) != 0) & (length(param$excludeCovariateIds) != 0)) {
covariates <- covariateData$covariates %>%
dplyr::filter(.data$covariateId %in% param$includeCovariateIds) %>%
dplyr::filter(!.data$covariateId %in% param$excludeCovariateIds) # does not work
} else if ( (length(param$includeCovariateIds) == 0) & (length(param$excludeCovariateIds) != 0)) {
covariates <- covariateData$covariates %>%
dplyr::filter(!.data$covariateId %in% param$excludeCovariateIds) # does not work
} else if ( (length(param$includeCovariateIds) != 0) & (length(param$excludeCovariateIds) == 0)) {
includeCovariateIds <- as.double(param$includeCovariateIds) # fixes odd dplyr issue with param
covariates <- covariateData$covariates %>%
dplyr::filter(.data$covariateId %in% includeCovariateIds)
} else {
covariates <- covariateData$covariates
}
return(covariates)
}
reparamTransferCoefs <- function(inCoefs) {
transferCoefs <- inCoefs %>%
dplyr::filter(grepl("-", .data$covariateIds))
transferCoefs$covariateIds <- substring(transferCoefs$covariateIds, 2)
originalCoefs <- inCoefs %>%
dplyr::filter(!grepl("-", .data$covariateIds))
coefs <- rbind(originalCoefs, transferCoefs)
coefs <- rowsum(coefs$betas, coefs$covariateIds)
coefs <- data.frame(betas = coefs, covariateIds = rownames(coefs), row.names = NULL)
return(coefs)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.