new change in calling directory in calibrate_sir

R/calibrate_sir.R

@@ -21,62 +21,62 @@
 #' @details
 #' The function determines which pre-trained CNN model to load based on the number of features (columns) in the input `data`. If `data` has 30 columns, it loads the `sir30-cnn.keras` model; if it has 60 columns, it loads the `sir60-cnn.keras` model. Ensure that the input data matches one of these expected formats to avoid errors.
 #' @export
-calibrate_sir <- function(data) {
-  library(keras3)
-  ans=preprocessing_data(data)
-  a=length(ans)
-  ans <- tensorflow::array_reshape(ans, dim = c(1, 1, a, 1))
-
-  if(a <=30){
-    model <- keras3::load_model(
-      system.file("models", "sir30-cnn.keras", package = "epiworldRcalibrate")
-      )
-  }
-  else{
-    model <- keras3::load_model(
-      system.file("models", "sir60-cnn.keras", package = "epiworldRcalibrate")
-      )
-  }
-  pred <- predict(model, x =ans ) |>
-    data.table::as.data.table() |>
-    data.table::setnames(c("preval","crate","ptran","prec"))
-  pred$crate=qlogis(pred$crate)
-
-  return(list(pred = pred))
-}
 # calibrate_sir <- function(data) {
-#   # Load required libraries
-#   library(tensorflow)
-#   library(data.table)
-#
-#   # Preprocess the data
-#   ans <- preprocessing_data(data)
-#   a <- length(ans)
-#   ans <- tensorflow::array_reshape(ans, dim = c(1, 1, a, 1))  # Reshape for the model
+#   library(keras3)
+#   ans=preprocessing_data(data)
+#   a=length(ans)
+#   ans <- tensorflow::array_reshape(ans, dim = c(1, 1, a, 1))
 #
-#   # Determine model file path
-#   model_path <- if (a <= 31) {
-#     system.file("models", "sir30-cnn.keras", package = "epiworldRcalibrate")
-#   } else {
-#     system.file("models", "sir60-cnn.keras", package = "epiworldRcalibrate")
+#   if(a <=30){
+#     model <- keras3::load_model(
+#       system.file("models", "sir30-cnn.keras", package = "epiworldRcalibrate")
+#       )
 #   }
-#
-#   # Check if the model file exists
-#   if (model_path == "") {
-#     stop("Model file not found. Please ensure the models are included in the 'epiworldRcalibrate' package.")
+#   else{
+#     model <- keras3::load_model(
+#       system.file("models", "sir60-cnn.keras", package = "epiworldRcalibrate")
+#       )
 #   }
-#
-#   # Load the model using tensorflow
-#   model <- tensorflow::tf$keras$models$load_model(model_path)
-#
-#   # Make predictions
-#   pred <- model$predict(ans) |>
+#   pred <- predict(model, x =ans ) |>
 #     data.table::as.data.table() |>
-#     data.table::setnames(c("preval", "crate", "ptran", "prec"))
-#
+#     data.table::setnames(c("preval","crate","ptran","prec"))
+#   pred$crate=qlogis(pred$crate)
 #
-#   # Return predictions as a list
 #   return(list(pred = pred))
 # }
-#
+calibrate_sir <- function(data) {
+  # Load required libraries
+  library(tensorflow)
+  library(data.table)
+
+  # Preprocess the data
+  ans <- preprocessing_data(data)
+  a <- length(ans)
+  ans <- tensorflow::array_reshape(ans, dim = c(1, 1, a, 1))  # Reshape for the model
+
+  # Determine model file path
+  model_path <- if (a <= 31) {
+    system.file("models", "sir30-cnn.keras", package = "epiworldRcalibrate")
+  } else {
+    system.file("models", "sir60-cnn.keras", package = "epiworldRcalibrate")
+  }
+
+  # Check if the model file exists
+  if (model_path == "") {
+    stop("Model file not found. Please ensure the models are included in the 'epiworldRcalibrate' package.")
+  }
+
+  # Load the model using tensorflow
+  model <- tensorflow::tf$keras$models$load_model(model_path)
+
+  # Make predictions
+  pred <- model$predict(ans) |>
+    data.table::as.data.table() |>
+    data.table::setnames(c("preval", "crate", "ptran", "prec"))
+
+
+  # Return predictions as a list
+  return(list(pred = pred))
+}
+