feat: check and fix all_sol_json & new pareto_df parameters inputs

R/R/inputs.R

@@ -274,6 +274,11 @@ robyn_inputs <- function(dt_input = NULL,
     # Check for no-variance columns on raw data (after removing not-used)
     check_novar(select(dt_input, -all_of(unused_vars)))
 
+    # Calculate total media spend used to model
+    paid_media_total <- dt_input[
+      rollingWindowEndWhich:rollingWindowLength, ] %>%
+      select(paid_media_vars) %>% sum()
+
     ## Collect input
     InputCollect <- list(
       dt_input = dt_input,
@@ -294,6 +299,7 @@ robyn_inputs <- function(dt_input = NULL,
       paid_media_vars = paid_media_vars,
       paid_media_signs = paid_media_signs,
       paid_media_spends = paid_media_spends,
+      paid_media_total = paid_media_total,
       mediaVarCount = mediaVarCount,
       exposure_vars = exposure_vars,
       organic_vars = organic_vars,
@@ -307,6 +313,7 @@ robyn_inputs <- function(dt_input = NULL,
       window_end = window_end,
       rollingWindowEndWhich = rollingWindowEndWhich,
       rollingWindowLength = rollingWindowLength,
+      totalObservations = nrow(dt_input),
       refreshAddedStart = refreshAddedStart,
       adstock = adstock,
       hyperparameters = hyperparameters,

R/R/json.R

@@ -15,7 +15,7 @@
 #' @param select_model Character. Which model ID do you want to export
 #' into the JSON file?
 #' @param dir Character. Existing directory to export JSON file to.
-#' @param all_sol_json Dataframe. Add all pareto solutions to json.
+#' @param pareto_df Dataframe. Save all pareto solutions to json file.
 #' @param ... Additional parameters.
 #' @examples
 #' \dontrun{
@@ -36,7 +36,7 @@ robyn_write <- function(InputCollect,
                         OutputModels = NULL,
                         export = TRUE,
                         quiet = FALSE,
-                        all_sol_json = NULL,
+                        pareto_df = NULL,
                         ...) {
   # Checks
   stopifnot(inherits(InputCollect, "robyn_inputs"))
@@ -70,6 +70,7 @@ robyn_write <- function(InputCollect,
     outputs <- list()
     outputs$select_model <- select_model
     outputs$ts_validation <- OutputCollect$OutputModels$ts_validation
+    outputs$export_timestamp <- Sys.time()
     outputs$run_time <- run_time
     outputs$outputs_time <- outputs_time
     outputs$total_time <- total_time
@@ -87,6 +88,9 @@ robyn_write <- function(InputCollect,
       )
     outputs$errors <- filter(OutputCollect$resultHypParam, .data$solID == select_model) %>%
       select(starts_with("rsq_"), starts_with("nrmse"), .data$decomp.rssd, .data$mape)
+    if ("clusters" %in% names(OutputCollect)) {
+      outputs$clusters <- OutputCollect$clusters$n_clusters
+    }
     outputs$hyper_values <- OutputCollect$resultHypParam %>%
       filter(.data$solID == select_model) %>%
       select(contains(HYPS_NAMES), dplyr::ends_with("_penalty"), any_of(HYPS_OTHERS)) %>%
@@ -109,15 +113,21 @@ robyn_write <- function(InputCollect,
   attr(ret, "json_file") <- filename
   if (export) {
     if (!quiet) message(sprintf(">> Exported model %s as %s", select_model, filename))
-    if (!is.null(all_sol_json)) {
-      all_c <- unique(all_sol_json$cluster)
-      all_sol_json <- lapply(all_c, function(x) {
-        (all_sol_json %>% filter(.data$cluster == x))$solID
-      })
-      names(all_sol_json) <- paste0("cluster", all_c)
-      ret[["InputCollect"]][["total_time"]] <- total_time
-      ret[["InputCollect"]][["total_iters"]] <- OutputModels$iterations * OutputModels$trials
-      ret[["OutputCollect"]][["all_sols"]] <- all_sol_json
+    if (!is.null(pareto_df)) {
+      if (!all(c("solID", "cluster") %in% names(pareto_df))) {
+        warning(paste(
+          "Input 'pareto_df' is not a valid data.frame;",
+          "must contain 'solID' and 'cluster' columns."))
+      } else {
+        all_c <- unique(pareto_df$cluster)
+        pareto_df <- lapply(all_c, function(x) {
+          (pareto_df %>% filter(.data$cluster == x))$solID
+        })
+        names(pareto_df) <- paste0("cluster", all_c)
+        ret[["InputCollect"]][["total_time"]] <- total_time
+        ret[["InputCollect"]][["total_iters"]] <- OutputModels$iterations * OutputModels$trials
+        ret[["OutputCollect"]][["all_sols"]] <- pareto_df
+      }
     }
     write_json(ret, filename, pretty = TRUE, digits = 10)
   }

R/R/model.R

@@ -175,6 +175,7 @@ robyn_run <- function(InputCollect = NULL,
   attr(OutputModels, "refresh") <- refresh
 
   if (TRUE) {
+    OutputModels$train_timestamp <- Sys.time()
     OutputModels$cores <- cores
     OutputModels$iterations <- iterations
     OutputModels$trials <- trials

R/R/outputs.R

@@ -228,18 +228,18 @@ robyn_outputs <- function(InputCollect, OutputModels,
         }
 
         if (all_sol_json) {
-          all_sol_json <- OutputCollect$resultHypParam %>%
+          pareto_df <- OutputCollect$resultHypParam %>%
             filter(!is.na(.data$cluster)) %>%
             select(c("solID", "cluster", "top_sol")) %>%
             arrange(.data$cluster, -.data$top_sol, .data$solID)
         } else {
-          all_sol_json <- NULL
+          pareto_df <- NULL
         }
         robyn_write(
           InputCollect = InputCollect,
           OutputModels = OutputModels,
           dir = plot_folder, quiet = quiet,
-          all_sol_json = all_sol_json
+          pareto_df = pareto_df, ...
         )
 
         # For internal use -> UI Code