merge and refactor gridCvDeep

.Rbuildignore

@@ -8,3 +8,4 @@
 ^extras$
 ^deploy.sh$
 ^compare_versions$
+^.mypy_cache$

.gitignore

@@ -10,3 +10,4 @@ renv.lock
 extras/
 .Renviron
 inst/python/__pycache__
+.mypy_cache

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-export(TrainingCache)
 export(fitEstimator)
 export(gridCvDeep)
 export(predictDeepEstimator)
@@ -10,6 +9,7 @@ export(setEstimator)
 export(setMultiLayerPerceptron)
 export(setResNet)
 export(setTransformer)
+export(trainingCache)
 importFrom(dplyr,"%>%")
 importFrom(reticulate,py_to_r)
 importFrom(reticulate,r_to_py)

R/Dataset.R

@@ -15,21 +15,22 @@
 # 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.
-createDataset <- function(data, labels, plpModel=NULL) {
+createDataset <- function(data, labels, plpModel = NULL) {
   path <- system.file("python", package = "DeepPatientLevelPrediction")
-  Dataset <- reticulate::import_from_path("Dataset", path = path)$Data
+  dataset <- reticulate::import_from_path("Dataset", path = path)$Data
   if (is.null(attributes(data)$path)) {
     # sqlite object
     attributes(data)$path <- attributes(data)$dbname
   }
   if (is.null(plpModel)) {
-    data <- Dataset(r_to_py(normalizePath(attributes(data)$path)), 
-                            r_to_py(labels$outcomeCount))
+    data <- dataset(r_to_py(normalizePath(attributes(data)$path)),
+                    r_to_py(labels$outcomeCount))
+  } else {
+    numericalFeatures <-
+      r_to_py(as.array(which(plpModel$covariateImportance$isNumeric)))
+    data <- dataset(r_to_py(normalizePath(attributes(data)$path)),
+                    numerical_features = numericalFeatures)
   }
-  else {
-    data <- Dataset(r_to_py(normalizePath(attributes(data)$path)),
-                    numerical_features = r_to_py(as.array(which(plpModel$covariateImportance$isNumeric)))  )
-  }
-
-  return(data)  
-}
+
+  return(data)
+}

R/DeepPatientLevelPrediction.R

@@ -18,7 +18,8 @@
 
 #' DeepPatientLevelPrediction
 #'
-#' @description A package containing deep learning extensions for developing prediction models using data in the OMOP CDM
+#' @description A package containing deep learning extensions for developing
+#' prediction models using data in the OMOP CDM
 #'
 #' @docType package
 #' @name DeepPatientLevelPrediction
@@ -28,9 +29,7 @@
 NULL
 
 .onLoad <- function(libname, pkgname) {
-  # use superassignment to update global reference 
+  # use superassignment to update global reference
   reticulate::configure_environment(pkgname)
   torch <<- reticulate::import("torch", delay_load = TRUE)
 }
-
-