#' Construct A Batch of Keras Models

#'

#' @inheritParams modeling_and_fitting

#' @param param_combination A parameter `matrix`/`data.frame` with each row representing the parameters

#' for run Keras model once. Column names should indicate parameter names and should be same as in modeling function.

#' [base::expand.grid()] may be very useful to generate it.

#' @param ... Other arguments passing to [modeling_and_fitting].

#'

#' @return a `tibble`.

#' @export

#'

#' @examples

#' load(system.file("extdata", "wang2020-input.RData",

#' package = "sigminer.prediction", mustWork = TRUE

#' ))

#' dat_list <- prepare_data(expo_all,

#' col_to_vars = c(paste0("Sig", 1:5), paste0("AbsSig", 1:5)),

#' col_to_label = "enrich_sig",

#' label_names = paste0("Sig", 1:5)

#' )

#' pc <- expand.grid(

#' c(10, 20, 50, 100),

#' c(0, 0.1, 0.2, 0.3, 0.4, 0.5),

#' c(10, 20, 50, 100),

#' c(0, 0.1, 0.2, 0.3, 0.4, 0.5)

#' )

#' colnames(pc) <- c(

#' "first_layer_unit", "second_layer_drop_rate",

#' "third_layer_unit", "fourth_layer_drop_rate"

#' )

#'

#' # Just use 2 rows for illustration

#' batch_res <- batch_modeling_and_fitting(dat_list, param_combination = pc %>% head(2))

#' batch_res

#'

#' tidy(batch_res)

batch_modeling_and_fitting <- function(data_list,

param_combination,

...) {

stopifnot(is.matrix(param_combination) | is.data.frame(param_combination))

if (is.matrix(param_combination)) {

if (is.null(colnames(param_combination))) {

stop("Colnames of param_combination must be set to map the parameters in 'modeling_and_fitting' like function!")

}

param_combination <- as.data.frame(param_combination)

}

args <- list(...)

args$data_list <- data_list

model_df <- dplyr::tibble()

for (i in 1:nrow(param_combination)) {

message("=> Running model with parameter combination #", i)

args_update <- c(args, param_combination[i, , drop = FALSE])

temp_df <- do.call("modeling_and_fitting", args = args_update)

print(temp_df)

model_df <- dplyr::bind_rows(model_df, temp_df)

}

return(model_df)

}