From 5a92835b596aec46ac34fc5282b4f08d43974368 Mon Sep 17 00:00:00 2001
From: Egill Fridgeirsson <egillax@gmail.com>
Date: Sun, 9 Oct 2022 14:35:57 +0200
Subject: [PATCH] update description

---
 DESCRIPTION   |  6 +++---
 R/Estimator.R | 18 +++++++++---------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 8fa61a7..b135257 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: DeepPatientLevelPrediction
 Type: Package
 Title: Deep Learning For Patient Level Prediction Using Data In The OMOP Common Data Model
 Version: 1.0.0
-Date: 29-08-2022
+Date: 09-10-2022
 Authors@R: c(
     person("Jenna", "Reps", email = "jreps@its.jnj.com", role = c("aut")), 
     person("Egill", "Fridgeirsson", email = "e.fridgeirsson@erasmusmc.nl", role = c("aut", "cre")),
@@ -23,7 +23,7 @@ Imports:
     data.table,
     FeatureExtraction (>= 3.0.0),
     ParallelLogger (>= 2.0.0),
-    PatientLevelPrediction,
+    PatientLevelPrediction, (>= 6.0.4)
     rlang,
     torch (>= 0.8.0)
 Suggests:
@@ -34,7 +34,7 @@ Suggests:
     plyr,  
     testthat
 Remotes:
-    ohdsi/PatientLevelPrediction@develop,
+    ohdsi/PatientLevelPrediction,
     ohdsi/FeatureExtraction,
     ohdsi/Eunomia
 RoxygenNote: 7.2.1
diff --git a/R/Estimator.R b/R/Estimator.R
index 8abb048..0ad632b 100644
--- a/R/Estimator.R
+++ b/R/Estimator.R
@@ -27,7 +27,6 @@
 #' @param optimizer             which optimizer to use
 #' @param scheduler             which learning rate scheduler to use
 #' @param criterion             loss function to use
-#' @param posWeight             If more weight should be added to positive labels during training - will result in miscalibrated models
 #' @param earlyStopping         If earlyStopping should be used which stops the training of your metric is not improving
 #' @param earlyStoppingMetric   Which parameter to use for early stopping
 #' @param patience              patience for earlyStopper
@@ -247,7 +246,6 @@ gridCvDeep <- function(mappedData,
       ParallelLogger::logInfo(paste0("Fold ", i))
       trainDataset <- torch::dataset_subset(dataset, indices = which(fold != i))
       testDataset <- torch::dataset_subset(dataset, indices = which(fold == i))
-      # fitParams$posWeight <- trainDataset$dataset$posWeight
       estimator <- Estimator$new(
         baseModel = baseModel,
         modelParameters = modelParams,
@@ -301,7 +299,6 @@ gridCvDeep <- function(mappedData,
   fitParams <- finalParam[fitParamNames]
   fitParams$epochs <- finalParam$learnSchedule$bestEpoch
   fitParams$batchSize <- batchSize
-  fitParams$posWeight <- dataset$posWeight
   # create the dir
   if (!dir.exists(file.path(modelLocation))) {
     dir.create(file.path(modelLocation), recursive = T)
@@ -388,7 +385,6 @@ Estimator <- R6::R6Class(
       self$learningRate <- self$itemOrDefaults(fitParameters, "learningRate", 1e-3)
       self$l2Norm <- self$itemOrDefaults(fitParameters, "weightDecay", 1e-5)
       self$batchSize <- self$itemOrDefaults(fitParameters, "batchSize", 1024)
-      self$posWeight <- self$itemOrDefaults(fitParameters, "posWeight", 1)
       self$prefix <- self$itemOrDefaults(fitParameters, "prefix", self$model$name)
 
       self$previousEpochs <- self$itemOrDefaults(fitParameters, "previousEpochs", 0)
@@ -399,9 +395,7 @@ Estimator <- R6::R6Class(
         lr = self$learningRate,
         weight_decay = self$l2Norm
       )
-      self$criterion <- criterion(torch::torch_tensor(self$posWeight,
-        device = self$device
-      ))
+      self$criterion <- criterion()
 
       self$scheduler <- scheduler(self$optimizer,
         patience = 1,
@@ -615,14 +609,20 @@ Estimator <- R6::R6Class(
       batchIndex <- 1:length(dataset)
       batchIndex <- split(batchIndex, ceiling(seq_along(batchIndex) / self$batchSize))
       torch::with_no_grad({
-        predictions <- c()
+        predictions <- torch::torch_empty(length(dataset), device=self$device)
         self$model$eval()
+        progressBar <- utils::txtProgressBar(style = 3)
+        ix <- 1
         coro::loop(for (b in batchIndex) {
           batch <- self$batchToDevice(dataset[b])
           target <- batch$target
           pred <- self$model(batch$batch)
-          predictions <- c(predictions, as.array(torch::torch_sigmoid(pred$cpu())))
+          predictions[b] <- torch::torch_sigmoid(pred)
+          utils::setTxtProgressBar(progressBar, ix / length(batchIndex))
+          ix <- ix + 1
         })
+        predictions <- as.array(predictions$cpu())
+        close(progressBar)
       })
       return(predictions)
     },