From 2ed39c8e26abb79347c19131478510ca1460715f Mon Sep 17 00:00:00 2001
From: Sima Najafzadehkhoei <u1418987@umail.utah.edu>
Date: Tue, 10 Dec 2024 15:12:38 -0700
Subject: [PATCH] new change in calling directory in calibrate_sir

---
 R/calibrate_sir.R | 100 +++++++++++++++++++++++-----------------------
 1 file changed, 50 insertions(+), 50 deletions(-)

diff --git a/R/calibrate_sir.R b/R/calibrate_sir.R
index fc84081..cba7ce5 100644
--- a/R/calibrate_sir.R
+++ b/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))
+# }
+#