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feat: added progress alerts #11

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19 changes: 17 additions & 2 deletions R/ds.Lasso.R
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Original file line number Diff line number Diff line change
Expand Up @@ -296,11 +296,13 @@ ds.LR_Lasso <- function (X, Y, lam, C, opts, datasources, nDigits){
#' @details Training a regularization tree with Lasso
#'
#' @import DSI
#' @importFrom cli cli_alert_info
#' @export
#' @author Han Cao
################################################################################
ds.Lasso_Train = function(X=NULL, Y=NULL, type="regress", nlambda=10, lam_ratio=0.01, lambda=NULL, C=0,
opts=list(init=0, maxIter=20, tol=0.01, ter=2), datasources=NULL, nDigits=10, intercept=F){
opts=list(init=0, maxIter=20, tol=0.01, ter=2), datasources=NULL, nDigits=10,
intercept=F, fold_n = NULL, fold_tot = NULL){

#intercept model
if (intercept){
Expand Down Expand Up @@ -339,6 +341,8 @@ ds.Lasso_Train = function(X=NULL, Y=NULL, type="regress", nlambda=10, lam_ratio=
#warm-start training procedure
optsTrain=opts
for(i in 1:length(lam_seq)){
cli_alert_info(sprintf("Testing lamda %s of %s, fold %s of %s", i, length(lam_seq), fold_n, fold_tot))

m=ds.LS_Lasso(X=X, Y=Y, lam=lam_seq[i], C=C, opts=optsTrain, datasources=datasources, nDigits=nDigits)
optsTrain$w0=m$w; optsTrain$init=1
fit$ws=cbind(fit$ws,m$w)
Expand All @@ -365,6 +369,7 @@ ds.Lasso_Train = function(X=NULL, Y=NULL, type="regress", nlambda=10, lam_ratio=
#warm-start training procedure
optsTrain=opts
for(i in 1:length(lam_seq)){
cli_alert_info(sprintf("Testing lamda %s of %s, fold %s of %s", i, length(lam_seq), fold_n, fold_tot))
m=ds.LR_Lasso(X=X, Y=Y, lam=lam_seq[i], C=C, opts=optsTrain, datasources=datasources, nDigits=nDigits)
optsTrain$w0=m$w; optsTrain$init=1
fit$ws=cbind(fit$ws,m$w)
Expand Down Expand Up @@ -477,6 +482,7 @@ ds.Lasso_CVCroSite = function(X=NULL, Y=NULL, type="regress", lam_ratio=0.01, nl
#' @details Cross-site cross-validation
#'
#' @import DSI
#' @importFrom cli cli_alert_info
#' @export
#' @author Han Cao
################################################################################
Expand Down Expand Up @@ -527,13 +533,18 @@ ds.Lasso_CVInSite = function(X=NULL, Y=NULL, type="regress", nfolds=10, lam_rati
mse_fold=vector()
lam_seq=vector()
for (i in 1:length(cvPar)){
cli_alert_info(sprintf("Making fold number %s", i))

ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Xtrain", symbol=X)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Ytrain", symbol=Y)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTest, newSymbol="Xtest", symbol=X)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTest, newSymbol="Ytest", symbol=Y)

fit=ds.Lasso_Train(X="Xtrain", Y="Ytrain", nlambda=nlambda, lam_ratio=lam_ratio, type="regress", opts=opts, C=C, lambda=lambda,
datasources=datasources, nDigits=nDigits)
datasources=datasources, nDigits=nDigits, fold_n = i, fold_tot = length(cvPar))

cli_alert_info("Calculating missing classification rate")

mse_task=sapply(1:nTasks, function(x) {
mse=ds.calcMSE(ws = fit$ws, datasourceTest = datasources[x], X="Xtest", Y="Ytest", average=F)
mse=mse[[1]]*nSubs[x]/sum(nSubs)
Expand All @@ -549,13 +560,17 @@ ds.Lasso_CVInSite = function(X=NULL, Y=NULL, type="regress", nfolds=10, lam_rati
mcr_fold=vector()
lam_seq=vector()
for (i in 1:length(cvPar)){
cli_alert_info(sprintf("Making fold number %s", i))
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Xtrain", symbol=X)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Ytrain", symbol=Y)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTest, newSymbol="Xtest", symbol=X)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTest, newSymbol="Ytest", symbol=Y)

fit=ds.Lasso_Train(X="Xtrain", Y="Ytrain", nlambda=nlambda, lam_ratio=lam_ratio, type="classify", opts=opts, C=C, lambda=lambda,
datasources=datasources, nDigits=nDigits)

cli_alert_info("Calculating missing classification rate")

mcr_task=sapply(1:nTasks, function(x) {
mcr=ds.calcMCR(ws = fit$ws, datasourceTest = datasources[x], X="Xtest", Y="Ytest", average=F)
mcr=mcr[[1]]*nSubs[x]/sum(nSubs)
Expand Down
12 changes: 10 additions & 2 deletions R/ds.LassoCov.R
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Original file line number Diff line number Diff line change
Expand Up @@ -319,7 +319,8 @@ ds.LR_LassoCov <- function (X, Y, lam, covar=NULL, opts, datasources, nDigits){
################################################################################

ds.LassoCov_Train = function(X=NULL, Y=NULL, type="regress", nlambda=10, lam_ratio=0.01, lambda=NULL, covar=NULL,
opts=list(init=0, maxIter=20, tol=0.01, ter=2), datasources=NULL, nDigits=10, intercept=F){
opts=list(init=0, maxIter=20, tol=0.01, ter=2), datasources=NULL, nDigits=10, intercept=F,
fold_n = NULL, fold_tot = NULL){


#intercept model
Expand Down Expand Up @@ -383,6 +384,7 @@ ds.LassoCov_Train = function(X=NULL, Y=NULL, type="regress", nlambda=10, lam_rat
#warm-start training procedure
optsTrain=opts
for(i in 1:length(lam_seq)){
cli_alert_info(sprintf("Testing lamda %s of %s, fold %s of %s", i, length(lam_seq), fold_n, fold_tot))
#----------Modifications for covariates adjustment
m=ds.LS_LassoCov(X=X, Y=Y, lam=lam_seq[i], covar=covar, opts=optsTrain, datasources=datasources, nDigits=nDigits)
#----------
Expand Down Expand Up @@ -431,6 +433,7 @@ ds.LassoCov_Train = function(X=NULL, Y=NULL, type="regress", nlambda=10, lam_rat
#warm-start training procedure
optsTrain=opts
for(i in 1:length(lam_seq)){
cli_alert_info(sprintf("Testing lamda %s of %s, fold %s of %s", i, length(lam_seq), fold_n, fold_tot))
#----------Modifications for covariates adjustment
m=ds.LR_LassoCov(X=X, Y=Y, lam=lam_seq[i], covar=covar, opts=optsTrain, datasources=datasources, nDigits=nDigits)
#----------
Expand Down Expand Up @@ -529,14 +532,16 @@ ds.LassoCov_CVInSite = function(X=NULL, Y=NULL, type="regress", nfolds=10, lam_r
mse_fold=vector()
lam_seq=vector()
for (i in 1:length(cvPar)){
cli_alert_info(sprintf("Making fold number %s", i))
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Xtrain", symbol=X)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Ytrain", symbol=Y)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTest, newSymbol="Xtest", symbol=X)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTest, newSymbol="Ytest", symbol=Y)
#----------Modifications for covariates adjustment
fit=ds.LassoCov_Train(X="Xtrain", Y="Ytrain", nlambda=nlambda, lam_ratio=lam_ratio, type="regress", opts=opts, covar=covar, lambda=lambda,
datasources=datasources, nDigits=nDigits)
datasources=datasources, nDigits=nDigits, fold_n = i, fold_tot = length(cvPar))
#----------
cli_alert_info("Calculating missing classification rate")
mse_task=sapply(1:nTasks, function(x) {
mse=ds.calcMSE(ws = fit$ws, datasourceTest = datasources[x], X="Xtest", Y="Ytest", average=F)
mse=mse[[1]]*nSubs[x]/sum(nSubs)
Expand All @@ -552,6 +557,8 @@ ds.LassoCov_CVInSite = function(X=NULL, Y=NULL, type="regress", nfolds=10, lam_r
mcr_fold=vector()
lam_seq=vector()
for (i in 1:length(cvPar)){
cli_alert_info(sprintf("Making fold number %s", i))

ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Xtrain", symbol=X)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTrain, newSymbol="Ytrain", symbol=Y)
ds.subsetSubjests(datasources, idx=cvPar[[i]]$cvTest, newSymbol="Xtest", symbol=X)
Expand All @@ -560,6 +567,7 @@ ds.LassoCov_CVInSite = function(X=NULL, Y=NULL, type="regress", nfolds=10, lam_r
fit=ds.LassoCov_Train(X="Xtrain", Y="Ytrain", nlambda=nlambda, lam_ratio=lam_ratio, type="classify", opts=opts, covar=covar, lambda=lambda,
datasources=datasources, nDigits=nDigits)
#----------
cli_alert_info("Calculating missing classification rate")
mcr_task=sapply(1:nTasks, function(x) {
mcr=ds.calcMCR(ws = fit$ws, datasourceTest = datasources[x], X="Xtest", Y="Ytest", average=F)
mcr=mcr[[1]]*nSubs[x]/sum(nSubs)
Expand Down