Merge with main

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: DeepPatientLevelPrediction
 Type: Package
 Title: Deep Learning For Patient Level Prediction Using Data In The OMOP Common Data Model
-Version: 2.0.1.9999
+Version: 2.0.2
 Date: 18-04-2023
 Authors@R: c(
     person("Egill", "Fridgeirsson", email = "[email protected]", role = c("aut", "cre")),

R/TrainingCache-class.R

@@ -72,7 +72,7 @@ trainingCache <- R6::R6Class(
       return(all(unlist(lapply(private$.paramPersistence$gridSearchPredictions,
                                function(x) !is.null(x$gridPerformance)))))
     },
-
+    
     #' @description
     #' Gets the last index from the cached grid search
     #' @returns Last grid search index

tests/testthat/setup.R

@@ -110,4 +110,4 @@ if (!dir.exists(fitEstimatorPath)) {
 fitEstimatorResults <- fitEstimator(trainData$Train,
                                     modelSettings = modelSettings,
                                     analysisId = 1,
-                                    analysisPath = fitEstimatorPath)
+                                    analysisPath = fitEstimatorPath)

tests/testthat/test-Transformer.R

@@ -90,6 +90,9 @@ test_that("transformer nn-module works", {
     dim_hidden = 32
   )
   output <- model(input)
+  expect_equal(output$shape[0], 10L)  
+  input$num <- reticulate::py_none()
+  output <- model(input)
   expect_equal(output$shape[0], 10L)
   input$num <- reticulate::py_none()
   output <- model(input)
@@ -164,3 +167,32 @@ test_that("numerical embedding works as expected", {
   expect_equal(out$shape[[2]], embeddings)
 
 })
+
+test_that("numerical embedding works as expected", {
+  embeddings <- 32L # size of embeddings
+  features <- 2L # number of numerical features
+  patients <- 9L 
+
+  numTensor <- torch$randn(c(patients, features))
+
+  numericalEmbeddingClass <- reticulate::import_from_path("ResNet", path=path)$NumericalEmbedding
+  numericalEmbedding <- numericalEmbeddingClass(num_embeddings = features,
+                                                embedding_dim = embeddings,
+                                                bias = TRUE)
+  out <- numericalEmbedding(numTensor)
+
+  # should be patients x features x embedding size
+  expect_equal(out$shape[[0]], patients)
+  expect_equal(out$shape[[1]], features)
+  expect_equal(out$shape[[2]], embeddings)
+
+  numericalEmbedding <- numericalEmbeddingClass(num_embeddings = features,
+                                                embedding_dim = embeddings,
+                                                bias = FALSE)
+
+  out <- numericalEmbedding(numTensor)
+  expect_equal(out$shape[[0]], patients)
+  expect_equal(out$shape[[1]], features)
+  expect_equal(out$shape[[2]], embeddings)
+
+  })