fix example and saving of modified estimator

NAMESPACE

@@ -0,0 +1,28 @@
+# Generated by roxygen2: do not edit by hand
+
+export(Dataset)
+export(Estimator)
+export(doubleLayerNN)
+export(fitDeepNNTorch)
+export(fitEstimator)
+export(fitTabNetTorch)
+export(gridCvDeep)
+export(predictDeepEstimator)
+export(predictDeepNN)
+export(predictTabNetTorch)
+export(setCIReNN)
+export(setCNNTorch)
+export(setCovNN)
+export(setCovNN2)
+export(setDeepNN)
+export(setDeepNNTorch)
+export(setRNNTorch)
+export(setResNet)
+export(setTabNetTorch)
+export(setTransformer)
+export(singleLayerNN)
+export(toSparseMDeep)
+export(toSparseRTorch)
+export(transferLearning)
+export(tripleLayerNN)
+importFrom(zeallot,"%<-%")

R/Estimator.R

@@ -46,7 +46,7 @@ fitEstimator <- function(
     trainData$labels <- merge(trainData$labels, trainData$fold, by = 'rowId')
   }
 
-  mappedData <- PatientLevelPredictionArrow::toSparseM(
+  mappedData <- PatientLevelPrediction::toSparseM(
     plpData = trainData,  
     map = NULL
   )
@@ -55,7 +55,7 @@ fitEstimator <- function(
   labels <- mappedData$labels
   covariateRef <- mappedData$covariateRef
 
-  outLoc <- PatientLevelPredictionArrow:::createTempModelLoc() # export
+  outLoc <- PatientLevelPrediction:::createTempModelLoc() # export
 
   cvResult <- do.call( 
     what = gridCvDeep,
@@ -245,7 +245,6 @@ gridCvDeep <- function(
         device = device
       )
 
-      browser()
       estimator$fit(
         trainDataset, 
         testDataset

R/ResNet.R

@@ -93,7 +93,7 @@ setResNet <- function(
     saveType = 'file',
     modelParamNames = c("numLayers", "sizeHidden", "hiddenFactor",
                          "residualDropout", "hiddenDropout", "sizeEmbedding"),
-    baseModel = ResNet
+    baseModel = 'ResNet'
   )
 
   results <- list(
@@ -129,6 +129,9 @@ sparseLinearLayer <- torch::nn_module(
     }
   },
   forward = function(input) {
+    if (input$dtype != torch::torch_float32()) {
+      input <- input$type_as(self$weight)
+    }
     torch::nnf_linear(input, self$weight, self$bias)
   }
 )

R/Transformer.R

@@ -42,7 +42,7 @@ setTransformer <- function(numBlocks=3, dimToken=96, dimOut=1,
     saveType = 'file',
     modelParamNames = c('numBlocks', 'dimToken', 'dimOut', 'numHeads',
                         'attDropout', 'ffnDropout', 'resDropout', 'dimHidden'),
-    baseModel = Transformer
+    baseModel = 'Transformer'
   )
 
   results <- list(

extras/example.R

@@ -1,10 +1,9 @@
 # testing code (requires sequential branch of FeatureExtraction):
 # rm(list = ls())
 library(FeatureExtraction)
-library(PatientLevelPredictionArrow)
+library(PatientLevelPrediction)
 library(DeepPatientLevelPrediction)
 
-arrow <- T
 data(plpDataSimulationProfile)
 sampleSize <- 1e4
 plpData <- simulatePlpData(
@@ -13,68 +12,39 @@ plpData <- simulatePlpData(
 )
 
 
-populationSet <- PatientLevelPredictionArrow::createStudyPopulationSettings(
+populationSet <- PatientLevelPrediction::createStudyPopulationSettings(
   requireTimeAtRisk = F, 
   riskWindowStart = 1, 
   riskWindowEnd = 365)
 
 
-# modelSettings <- PatientLevelPrediction::setGradientBoostingMachine(ntrees = 100, nthread = 16, 
-#                                             earlyStopRound = 25, maxDepth = 6,
-#                                             minChildWeight = 1, learnRate = 0.3,
-#                                             seed = 42)
-
-# modelSettings <- PatientLevelPredictionArrow::setLassoLogisticRegression()
-
-modelSettings <- DeepPatientLevelPrediction::setTabNetTorch(device='cuda:0', randomSamples = 1,
-                                                            batchSize = 32)
-
-if (arrow) {
-  res2 <- runPlp(
-  plpData = plpData,
-  outcomeId = 3,
-  modelSettings = modelSettings,
-  analysisId = 'Test',
-  analysisName = 'Testing ARrow',
-  populationSettings = populationSet,
-  splitSettings = createDefaultSplitSetting(),
-  sampleSettings = createSampleSettings(),  # none
-  featureEngineeringSettings = createFeatureEngineeringSettings(), # none
-  preprocessSettings = createPreprocessSettings(),
-  logSettings = createLogSettings(verbosity='TRACE'),
-  executeSettings = createExecuteSettings(
-    runSplitData = T,
-    runSampleData = F,
-    runfeatureEngineering = F,
-    runPreprocessData = T,
-    runModelDevelopment = T,
-    runCovariateSummary = T
-  ),
-  saveDirectory = '~/test/arrow_new_plp/'
+modelSettings <- setResNet(numLayers = 2, sizeHidden = 64, hiddenFactor = 1,
+                          residualDropout = 0, hiddenDropout = 0.2, normalization = 'BatchNorm',
+                          activation = 'RelU', sizeEmbedding = 64, weightDecay = 1e-6,
+                          learningRate = 3e-4, seed = 42, hyperParamSearch = 'random',
+                          randomSample = 1, device = 'cuda:0',batchSize = 32,epochs = 1)
+
+res2 <- PatientLevelPrediction::runPlp(
+plpData = plpData,
+outcomeId = 3,
+modelSettings = modelSettings,
+analysisId = 'Test',
+analysisName = 'Testing DeepPlp',
+populationSettings = populationSet,
+splitSettings = createDefaultSplitSetting(),
+sampleSettings = createSampleSettings(),  # none
+featureEngineeringSettings = createFeatureEngineeringSettings(), # none
+preprocessSettings = createPreprocessSettings(),
+logSettings = createLogSettings(verbosity='TRACE'),
+executeSettings = createExecuteSettings(
+  runSplitData = T,
+  runSampleData = F,
+  runfeatureEngineering = F,
+  runPreprocessData = T,
+  runModelDevelopment = T,
+  runCovariateSummary = T
+),
+saveDirectory = '~/test/new_plp/'
 )
-} else {
-  library(PatientLevelPrediction)
-  res2 <- PatientLevelPrediction::runPlp(
-  plpData = plpData,
-  outcomeId = 3,
-  modelSettings = modelSettings,
-  analysisId = 'Test',
-  analysisName = 'Testing Original',
-  populationSettings = populationSet,
-  splitSettings = createDefaultSplitSetting(),
-  sampleSettings = createSampleSettings(),  # none
-  featureEngineeringSettings = createFeatureEngineeringSettings(), # none
-  preprocessSettings = createPreprocessSettings(),
-  logSettings = createLogSettings(verbosity='TRACE'),
-  executeSettings = createExecuteSettings(
-    runSplitData = T,
-    runSampleData = F,
-    runfeatureEngineering = F,
-    runPreprocessData = T,
-    runModelDevelopment = T,
-    runCovariateSummary = T
-  ),
-  saveDirectory = '~/test/new_plp/'
-  )
-}
+