Update function run_treeppl

DESCRIPTION

@@ -16,7 +16,9 @@ Imports:
     dplyr,
     jsonlite,
     magrittr,
-    rlang
+    rlang,
+    strex,
+    tidyr
 Suggests: 
     ggplot2,
     knitr,

R/run_treeppl.R

@@ -3,7 +3,7 @@
 #' @param dir The directory that contains the TreePPL code and input file. The executable and the output files will be written here.
 #' @param source Name of file with TreePPL program.
 #' @param method Inference method to be used.
-#' @param data Name of object with input data.
+#' @param data_path Path to JSON file with input data.
 #' @param samples The number of samples during inference.
 #'
 #' @return A data frame with sampled values, log weights, normalized weights, and the normalizing constant for all samples.
@@ -20,39 +20,29 @@
 #'     ggplot2::theme_bw()
 #'   }
 #' }
-run_treeppl <- function(dir = NULL, source = NULL, method = "smc-bpf", data = NULL, samples = 1000) { # smc-apf
+run_treeppl <- function(dir = NULL, source = NULL, method = "smc-bpf", data_path = NULL, samples = 1000) { # smc-apf
 
   # check inputs
 
+  if(method == "smc-apf") samples <- samples + 1
+
   # Compile program
   system2(command = "tpplc", args = c(paste0(dir,"/", source),
                                       paste0("-m ", method),
                                       paste0("--output ", dir,"/out")))
   # which arguments are necessary other than method?
   # should the executable go to a temporary folder and be delete afterwards?
 
-  # write json with input data
-  input_json <- jsonlite::toJSON(data, dataframe = "columns")
-  write(input_json, paste0(dir,"/input.json"))
-
   # run
   system2(command = paste0(dir,"/out"),
-          args = c(paste0(dir,"/input.json"),
+          args = c(data_path,
                    paste0(samples, " 1")),
           stdout = paste0(dir,"/stdout.json")
   )
 
   # read output
-  output <- jsonlite::fromJSON(paste0(dir,"/stdout.json")) %>%
-    as.data.frame() %>%
-    dplyr::mutate(nweights = norm_weights(.data$weights))
+  output <- read_treeppl_output(paste0(dir,"/stdout.json"))
 
   return(output)
 }
 
-# Function to get normalize weights from log weights
-norm_weights <- function(weights_vector) { # input log weights from TreePPL
-  lweights <- as.numeric(weights_vector) # making sure weights are numeric vector
-  weights <- exp(lweights - max(lweights)) # exponentiate the difference between the weight and the maximum of the weights
-  weights / sum(weights) # normalising weights
-}

R/treeppl_input.R

@@ -0,0 +1,23 @@
+#' Prepare input for inference with TreePPL
+#'
+#' This function takes R objects and writes out an JSON file to be used by `run_treeppl()`.
+#'
+#' @param object_list A list of R objects to be included in the JSON input file.
+#' @param file_path The complete path to where the JSON file is to be written.
+#'
+#' @return Path to a JSON file
+#' @export
+#'
+#' @examples
+treeppl_input <- function(object_list, file_path){
+
+  # process one element at a time?
+
+  # figure out the class and if anything special is necessary
+
+  # write json with input data
+  input_json <- jsonlite::toJSON(data, dataframe = "columns")
+  write(input_json, paste0(dir,"/input.json"))
+
+
+}

vignettes/coin-example.Rmd

@@ -27,8 +27,10 @@ Run the `coin.tppl` TreePPL program using the created input data.
 ```{r, eval=FALSE}
 coinflips <- tibble(coinflips = sample(c(TRUE, FALSE), 20, replace = TRUE))
 
+input <- treeppl_input(coinflips)
+
 output <- run_treeppl(dir = system.file("extdata", package = "treepplr"), 
-                      source = "coin.tppl", data = coinflips, samples = 100)
+                      source = "coin.tppl", data = input, samples = 100)
 
 ```