R/wt_rnn.R
In MoritzFeigl/wateRtemp: Water temperature prediction modelling
Defines functions
wt_rnn
Documented in
wt_rnn
#' wt_rnn
#'
#' Recurrent neural network implementation for stream water temperature prediction including Bayesian hyperparameter optimization. All results are stored automatically in the folder catchment/model_name.
#'
#' @param train_data Data frame containing training data created by using wt_preprocessing()
#' @param test_data Data frame containing test data created by using wt_preprocessing()
#' @param type RNN cell type to use. Can be either "LSTM" for the Long short-term model, or "GRU" for the Gated recurrent unit.
#' @param catchment Catchment name as string, used for storing results in current working directory.
#' @param model_name Name of this particular model run as string, used for storing results in the catchment folder.
#' @param seed Random seed.
#' @param n_iter Number of iteration steps for bayesian hyperparameter optimization.
#' @param n_random_initial_points Number of sampled initial random points for bayesian hyperparameter optimization
#' @param epochs integer. Number of training epochs
#' @param early_stopping_patience Integer. Early stopping patience, i.e. the number of epochs with no improvement to waite before stopping the training
#' @param ensemble_runs Number of ensembles used for making the finel model.
#' @param bounds_layers Vector containing the lower and upper bound of the numbers of layers used in the bayesian hyperparameter optimization.
#' @param bounds_units Vector containing the lower and upper bound of the numbers of units used in the bayesian hyperparameter optimization.
#' @param bounds_dropout Vector containing the lower and upper bound of the numbers of dropout used in the bayesian hyperparameter optimization.
#' @param bounds_batch_size Vector containing the lower and upper bound of the numbers of batch size used in the bayesian hyperparameter optimization.
#' @param bounds_timesteps Vector containing the lower and upper bound of the numbers of timesteps used in the bayesian hyperparameter optimization.
#' @param initial_grid_from_model_scores logical. Should previous results be used as initial grid for the hyperparameter optimization? These have to be stored in the model_name folder under model_scores.csv
#'
#' @export
#'
#' @examples
#' \dontrun{
#' data(test_catchment)
#' wt_preprocess(test_catchment)
#' train_data <- feather::read_feather("test_catchment/train_data.feather")
#' test_data <- feather::read_feather("test_catchment/test_data.feather")
#'
#' wt_rnn(train_data, test_data, "GRU", "test_catchment", "standard_rnn_gru")
#'}
wt_rnn <- function(train_data,
test_data = NULL,
type = "LSTM",
catchment = NULL,
model_name = NULL,
seed = NULL,
n_iter = 40,
n_random_initial_points = 20,
epochs = 100,
early_stopping_patience = 5,
ensemble_runs = 5,
bounds_layers = c(1, 5),
bounds_units = c(5, 300),
bounds_dropout = c(0, 0.4),
bounds_batch_size = c(5, 150),
bounds_timesteps = c(5, 200),
initial_grid_from_model_scores = TRUE){
# checks
model_short <- "RNN"
general_checks(catchment, train_data, test_data, model_name, type, cv_mode = NULL, model_short)
# create folders
folder_structure(catchment, model_short, model_name, type)
# Start message
start_message(catchment, model_short)
# start time
start_time <- Sys.time()
# get wt and date
train <- train_data[, which(colnames(train_data) %in% c("date", "wt"))]
if(!is.null(test_data)){
test <- test_data[, which(colnames(test_data) %in% c("date", "wt"))]
}
# remove NA rows
na_train <- which(is.na(train_data), arr.ind = TRUE)
if(nrow(na_train) > 0) train_data <- train_data[-unique(na_train[,1]),]
if(!is.null(test_data)) {
na_test <- which(is.na(test_data), arr.ind = TRUE)
if(nrow(na_test) > 0) test_data <- test_data[-na_test[,1],]
}
# train/val split
part_training <- nrow(train_data)/4 * 3
train_length <- floor(part_training)
nn_val <- train_data[(train_length + 1):nrow(train_data), ]
nn_train <- train_data[1:train_length, ]
# Feature scaling
date_col <- which(names(nn_train) == "date")
train_means <- apply(nn_train[, -date_col], 2, mean, na.rm = TRUE)
train_sds <- apply(nn_train[, -date_col], 2, sd, na.rm = TRUE)
features_to_scale <- colnames(nn_train)[!(colnames(nn_train) %in% c("date", "wt"))]
for(col in features_to_scale){
nn_train[, col] <- (nn_train[, col] - train_means[col]) / train_sds[col]
train_data[, col] <- (train_data[, col] - train_means[col]) / train_sds[col]
nn_val[, col] <- (nn_val[, col] - train_means[col]) / train_sds[col]
if(!is.null(test_data)){
test_data[, col] <- (test_data[, col] - train_means[col]) / train_sds[col]
}
}
# check if there are time gaps in the data -> split
utils::capture.output({
full_train_split <- data_splitter_for_rnn(train_data,
data_name = "train", catchment = catchment)
})
train_split <- data_splitter_for_rnn(nn_train, data_name = "train",
catchment = catchment)
val_split <- data_splitter_for_rnn(nn_val, data_name = "validation",
catchment = catchment)
if(!is.null(test_data)){
test_split <- data_splitter_for_rnn(test_data, data_name = "test",
catchment = catchment)
}
# split in x & y in
x_train <- lapply(train_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_train <- lapply(train_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
x_full_train <- lapply(full_train_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_full_train <- lapply(full_train_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
x_val <- lapply(val_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_val <- lapply(val_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
if(!is.null(test_data)){
x_test <- lapply(test_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_test <- lapply(test_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
} else {
x_test <- y_test <- test <- NULL
}
cat("Mean and standard deviation used for feature scaling are saved under",
paste0(catchment, "/", model_short, "/", type, "/",
model_name, "/scaling_values.csv\n"))
suppressWarnings(
utils::write.csv(rbind(train_means, train_sds),
paste0(catchment, "/",model_short, "/", type, "/",
model_name, "/scaling_values.csv"))
)
# initial value for flag -> if additional initial_grid points should be calculated
ini_grid_cal_flag <- FALSE
# if there are no model score available -> set initial_grid_from_model_scores = FALSE
if(initial_grid_from_model_scores){
if(!("hyperpar_opt_scores.csv" %in%
list.files(paste0(catchment, "/", model_short, "/", type, "/", model_name)))) {
initial_grid_from_model_scores <- FALSE
}
}
if(initial_grid_from_model_scores){
# get initial grid for optimization from the previous calculated model_scores
cat("Using existing scores as initial grid for the Bayesian Optimization\n")
hyperpar_opt_scores_csv <- utils::read.csv(
paste0(catchment, "/", model_short, "/", type, "/",
model_name, "/hyperpar_opt_scores.csv"),
stringsAsFactors = FALSE)
initial_grid <- hyperpar_opt_scores_csv[c("layers", "units", "dropout", "batch_size",
"timesteps", "cv_or_validation_RMSE")]
if(nrow(initial_grid) < n_random_initial_points) ini_grid_cal_flag <- TRUE
}
# should a random grid be calculated first
if(!initial_grid_from_model_scores | ini_grid_cal_flag) {
set.seed(seed)
n_random <- ifelse(ini_grid_cal_flag,
n_random_initial_points - nrow(initial_grid),
n_random_initial_points)
grid <- data.frame(
"layers" = replicate(n = n_random,
sample(
x = seq(bounds_layers[1], bounds_layers[2]),
size = 1)),
"units" = replicate(n = n_random,
sample(
x = seq(bounds_units[1], bounds_units[2]), size = 1)),
"dropout" = replicate(n = n_random,
sample(
x = seq(bounds_dropout[1], bounds_dropout[2], by = 0.025),
size = 1)),
"batch_size" = replicate(n = n_random,
sample(
x = seq(bounds_batch_size[1], bounds_batch_size[2]),
size = 1)),
"timesteps" = replicate(n = n_random,
sample(
x = seq(bounds_timesteps[1], bounds_timesteps[2]),
size = 1))
)
cat(paste0("\nRandom hyperparameter sampling:\n"))
# run initial grid models
grid_results <- mapply(
wt_nn,
batch_size = grid$batch_size,
layers = grid$layers,
units = grid$units,
dropout = grid$dropout,
timesteps = grid$timesteps,
ensemble_runs = 1,
MoreArgs = list(catchment = catchment,
x_train = x_train, y_train = y_train,
x_val = x_val, y_val = y_val,
x_full_train = x_full_train,
y_full_train = y_full_train,
epochs = epochs,
early_stopping_patience = early_stopping_patience,
test = test,
model_short = model_short,
model_name = model_name,
seed = seed,
nn_type = "RNN",
type = type,
n_features = (ncol(train_data)-2)
))
# Define initial_grid for bayesian hyperaparameter optimization
if(ini_grid_cal_flag){
additional_grid_points <- cbind(grid, grid_results)
names(additional_grid_points) <- names(initial_grid)
initial_grid <- rbind(initial_grid, additional_grid_points)
} else {
initial_grid <- cbind(grid, grid_results)
}
}
# Bayesian Hyperparameter optimization
cat("Bayesian Hyperparameter Optimization:\n")
if(nrow(initial_grid) > n_random_initial_points){
n_iter <- n_iter - (nrow(initial_grid) - n_random_initial_points)
cat(nrow(initial_grid) - n_random_initial_points,
"iterations were already computed\n")
}
colnames(initial_grid)[ncol(initial_grid)] <- "Value"
if(n_iter > 0){
# function to be optimized
Bopt_nn_model <- function(layers, units, dropout, batch_size, timesteps) {
results <- wt_nn(
x_train = x_train,
y_train = y_train,
x_val = x_val,
y_val = y_val,
x_test = x_test,
y_test = y_test,
x_full_train = x_full_train,
y_full_train = y_full_train,
catchment = catchment,
layers = layers,
units = units,
dropout = dropout,
batch_size = batch_size,
timesteps = timesteps,
epochs = epochs,
early_stopping_patience = early_stopping_patience,
ensemble_runs = 1,
model_short = model_short,
model_name = model_name,
seed = seed,
nn_type = "RNN",
type = type,
n_features = (ncol(train_data)-2))
return(list("Score" = results*-1, "Pred" = 0))
}
initial_grid$Value <- initial_grid$Value * -1
set.seed(seed)
Bopt_nn <- rBayesianOptimization::BayesianOptimization(
Bopt_nn_model,
bounds = list(layers = as.integer(bounds_layers),
units = as.integer(bounds_units),
dropout = bounds_dropout,
batch_size = as.integer(bounds_batch_size),
timesteps = as.integer(bounds_timesteps)),
n_iter = n_iter,
init_grid_dt = initial_grid,
acq = "ucb", kappa = 2.576, eps = 0.0,
verbose = TRUE)
all_model_results <- Bopt_nn$History
} else {
# if all iterations are done -> use initial grid
all_model_results <- initial_grid
all_model_results$Value <- all_model_results$Value * -1
}
# run best model as ensemble and save results
cat("Run the best performing model as ensemble:\n")
. = NULL
top_n_model_results <- all_model_results %>%
dplyr::top_n(n = 1, wt = "Value") %>%
dplyr::mutate("dropout" = round(., 2)) %>%
dplyr::select(-"Value")
if(nrow(top_n_model_results) != 1) top_n_model_results <- top_n_model_results[1, ]
set.seed(seed)
wt_nn(
layers = top_n_model_results$layers,
units = top_n_model_results$units,
dropout = top_n_model_results$dropout,
batch_size = top_n_model_results$batch_size,
timesteps = top_n_model_results$timesteps,
x_train = x_train,
y_train = y_train,
x_val = x_val,
y_val = y_val,
x_test = x_test,
y_test = y_test,
catchment = catchment,
epochs = epochs,
early_stopping_patience = early_stopping_patience,
ensemble_runs = ensemble_runs,
model_short = model_short,
model_name = model_name,
save_model_and_prediction = TRUE,
na_test = na_test,
na_train = na_train,
x_full_train = x_full_train,
y_full_train = y_full_train,
start_time = start_time,
seed = seed,
nn_type = "RNN",
test = test,
train = train,
type = type,
n_features = (ncol(train_data)-2),
full_train_split = full_train_split,
test_split = test_split)
}
MoritzFeigl/wateRtemp documentation built on Sept. 6, 2021, 6:58 a.m.
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Defines functions wt_rnn
Documented in wt_rnn
#' wt_rnn
#'
#' Recurrent neural network implementation for stream water temperature prediction including Bayesian hyperparameter optimization. All results are stored automatically in the folder catchment/model_name.
#'
#' @param train_data Data frame containing training data created by using wt_preprocessing()
#' @param test_data Data frame containing test data created by using wt_preprocessing()
#' @param type RNN cell type to use. Can be either "LSTM" for the Long short-term model, or "GRU" for the Gated recurrent unit.
#' @param catchment Catchment name as string, used for storing results in current working directory.
#' @param model_name Name of this particular model run as string, used for storing results in the catchment folder.
#' @param seed Random seed.
#' @param n_iter Number of iteration steps for bayesian hyperparameter optimization.
#' @param n_random_initial_points Number of sampled initial random points for bayesian hyperparameter optimization
#' @param epochs integer. Number of training epochs
#' @param early_stopping_patience Integer. Early stopping patience, i.e. the number of epochs with no improvement to waite before stopping the training
#' @param ensemble_runs Number of ensembles used for making the finel model.
#' @param bounds_layers Vector containing the lower and upper bound of the numbers of layers used in the bayesian hyperparameter optimization.
#' @param bounds_units Vector containing the lower and upper bound of the numbers of units used in the bayesian hyperparameter optimization.
#' @param bounds_dropout Vector containing the lower and upper bound of the numbers of dropout used in the bayesian hyperparameter optimization.
#' @param bounds_batch_size Vector containing the lower and upper bound of the numbers of batch size used in the bayesian hyperparameter optimization.
#' @param bounds_timesteps Vector containing the lower and upper bound of the numbers of timesteps used in the bayesian hyperparameter optimization.
#' @param initial_grid_from_model_scores logical. Should previous results be used as initial grid for the hyperparameter optimization? These have to be stored in the model_name folder under model_scores.csv
#'
#' @export
#'
#' @examples
#' \dontrun{
#' data(test_catchment)
#' wt_preprocess(test_catchment)
#' train_data <- feather::read_feather("test_catchment/train_data.feather")
#' test_data <- feather::read_feather("test_catchment/test_data.feather")
#'
#' wt_rnn(train_data, test_data, "GRU", "test_catchment", "standard_rnn_gru")
#'}
wt_rnn <- function(train_data,
test_data = NULL,
type = "LSTM",
catchment = NULL,
model_name = NULL,
seed = NULL,
n_iter = 40,
n_random_initial_points = 20,
epochs = 100,
early_stopping_patience = 5,
ensemble_runs = 5,
bounds_layers = c(1, 5),
bounds_units = c(5, 300),
bounds_dropout = c(0, 0.4),
bounds_batch_size = c(5, 150),
bounds_timesteps = c(5, 200),
initial_grid_from_model_scores = TRUE){
# checks
model_short <- "RNN"
general_checks(catchment, train_data, test_data, model_name, type, cv_mode = NULL, model_short)
# create folders
folder_structure(catchment, model_short, model_name, type)
# Start message
start_message(catchment, model_short)
# start time
start_time <- Sys.time()
# get wt and date
train <- train_data[, which(colnames(train_data) %in% c("date", "wt"))]
if(!is.null(test_data)){
test <- test_data[, which(colnames(test_data) %in% c("date", "wt"))]
}
# remove NA rows
na_train <- which(is.na(train_data), arr.ind = TRUE)
if(nrow(na_train) > 0) train_data <- train_data[-unique(na_train[,1]),]
if(!is.null(test_data)) {
na_test <- which(is.na(test_data), arr.ind = TRUE)
if(nrow(na_test) > 0) test_data <- test_data[-na_test[,1],]
}
# train/val split
part_training <- nrow(train_data)/4 * 3
train_length <- floor(part_training)
nn_val <- train_data[(train_length + 1):nrow(train_data), ]
nn_train <- train_data[1:train_length, ]
# Feature scaling
date_col <- which(names(nn_train) == "date")
train_means <- apply(nn_train[, -date_col], 2, mean, na.rm = TRUE)
train_sds <- apply(nn_train[, -date_col], 2, sd, na.rm = TRUE)
features_to_scale <- colnames(nn_train)[!(colnames(nn_train) %in% c("date", "wt"))]
for(col in features_to_scale){
nn_train[, col] <- (nn_train[, col] - train_means[col]) / train_sds[col]
train_data[, col] <- (train_data[, col] - train_means[col]) / train_sds[col]
nn_val[, col] <- (nn_val[, col] - train_means[col]) / train_sds[col]
if(!is.null(test_data)){
test_data[, col] <- (test_data[, col] - train_means[col]) / train_sds[col]
}
}
# check if there are time gaps in the data -> split
utils::capture.output({
full_train_split <- data_splitter_for_rnn(train_data,
data_name = "train", catchment = catchment)
})
train_split <- data_splitter_for_rnn(nn_train, data_name = "train",
catchment = catchment)
val_split <- data_splitter_for_rnn(nn_val, data_name = "validation",
catchment = catchment)
if(!is.null(test_data)){
test_split <- data_splitter_for_rnn(test_data, data_name = "test",
catchment = catchment)
}
# split in x & y in
x_train <- lapply(train_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_train <- lapply(train_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
x_full_train <- lapply(full_train_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_full_train <- lapply(full_train_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
x_val <- lapply(val_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_val <- lapply(val_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
if(!is.null(test_data)){
x_test <- lapply(test_split,
function(x) x %>% dplyr::select(-"wt", -date) %>% as.matrix())
y_test <- lapply(test_split,
function(x) x %>% dplyr::select("wt") %>% as.matrix())
} else {
x_test <- y_test <- test <- NULL
}
cat("Mean and standard deviation used for feature scaling are saved under",
paste0(catchment, "/", model_short, "/", type, "/",
model_name, "/scaling_values.csv\n"))
suppressWarnings(
utils::write.csv(rbind(train_means, train_sds),
paste0(catchment, "/",model_short, "/", type, "/",
model_name, "/scaling_values.csv"))
)
# initial value for flag -> if additional initial_grid points should be calculated
ini_grid_cal_flag <- FALSE
# if there are no model score available -> set initial_grid_from_model_scores = FALSE
if(initial_grid_from_model_scores){
if(!("hyperpar_opt_scores.csv" %in%
list.files(paste0(catchment, "/", model_short, "/", type, "/", model_name)))) {
initial_grid_from_model_scores <- FALSE
}
}
if(initial_grid_from_model_scores){
# get initial grid for optimization from the previous calculated model_scores
cat("Using existing scores as initial grid for the Bayesian Optimization\n")
hyperpar_opt_scores_csv <- utils::read.csv(
paste0(catchment, "/", model_short, "/", type, "/",
model_name, "/hyperpar_opt_scores.csv"),
stringsAsFactors = FALSE)
initial_grid <- hyperpar_opt_scores_csv[c("layers", "units", "dropout", "batch_size",
"timesteps", "cv_or_validation_RMSE")]
if(nrow(initial_grid) < n_random_initial_points) ini_grid_cal_flag <- TRUE
}
# should a random grid be calculated first
if(!initial_grid_from_model_scores | ini_grid_cal_flag) {
set.seed(seed)
n_random <- ifelse(ini_grid_cal_flag,
n_random_initial_points - nrow(initial_grid),
n_random_initial_points)
grid <- data.frame(
"layers" = replicate(n = n_random,
sample(
x = seq(bounds_layers[1], bounds_layers[2]),
size = 1)),
"units" = replicate(n = n_random,
sample(
x = seq(bounds_units[1], bounds_units[2]), size = 1)),
"dropout" = replicate(n = n_random,
sample(
x = seq(bounds_dropout[1], bounds_dropout[2], by = 0.025),
size = 1)),
"batch_size" = replicate(n = n_random,
sample(
x = seq(bounds_batch_size[1], bounds_batch_size[2]),
size = 1)),
"timesteps" = replicate(n = n_random,
sample(
x = seq(bounds_timesteps[1], bounds_timesteps[2]),
size = 1))
)
cat(paste0("\nRandom hyperparameter sampling:\n"))
# run initial grid models
grid_results <- mapply(
wt_nn,
batch_size = grid$batch_size,
layers = grid$layers,
units = grid$units,
dropout = grid$dropout,
timesteps = grid$timesteps,
ensemble_runs = 1,
MoreArgs = list(catchment = catchment,
x_train = x_train, y_train = y_train,
x_val = x_val, y_val = y_val,
x_full_train = x_full_train,
y_full_train = y_full_train,
epochs = epochs,
early_stopping_patience = early_stopping_patience,
test = test,
model_short = model_short,
model_name = model_name,
seed = seed,
nn_type = "RNN",
type = type,
n_features = (ncol(train_data)-2)
))
# Define initial_grid for bayesian hyperaparameter optimization
if(ini_grid_cal_flag){
additional_grid_points <- cbind(grid, grid_results)
names(additional_grid_points) <- names(initial_grid)
initial_grid <- rbind(initial_grid, additional_grid_points)
} else {
initial_grid <- cbind(grid, grid_results)
}
}
# Bayesian Hyperparameter optimization
cat("Bayesian Hyperparameter Optimization:\n")
if(nrow(initial_grid) > n_random_initial_points){
n_iter <- n_iter - (nrow(initial_grid) - n_random_initial_points)
cat(nrow(initial_grid) - n_random_initial_points,
"iterations were already computed\n")
}
colnames(initial_grid)[ncol(initial_grid)] <- "Value"
if(n_iter > 0){
# function to be optimized
Bopt_nn_model <- function(layers, units, dropout, batch_size, timesteps) {
results <- wt_nn(
x_train = x_train,
y_train = y_train,
x_val = x_val,
y_val = y_val,
x_test = x_test,
y_test = y_test,
x_full_train = x_full_train,
y_full_train = y_full_train,
catchment = catchment,
layers = layers,
units = units,
dropout = dropout,
batch_size = batch_size,
timesteps = timesteps,
epochs = epochs,
early_stopping_patience = early_stopping_patience,
ensemble_runs = 1,
model_short = model_short,
model_name = model_name,
seed = seed,
nn_type = "RNN",
type = type,
n_features = (ncol(train_data)-2))
return(list("Score" = results*-1, "Pred" = 0))
}
initial_grid$Value <- initial_grid$Value * -1
set.seed(seed)
Bopt_nn <- rBayesianOptimization::BayesianOptimization(
Bopt_nn_model,
bounds = list(layers = as.integer(bounds_layers),
units = as.integer(bounds_units),
dropout = bounds_dropout,
batch_size = as.integer(bounds_batch_size),
timesteps = as.integer(bounds_timesteps)),
n_iter = n_iter,
init_grid_dt = initial_grid,
acq = "ucb", kappa = 2.576, eps = 0.0,
verbose = TRUE)
all_model_results <- Bopt_nn$History
} else {
# if all iterations are done -> use initial grid
all_model_results <- initial_grid
all_model_results$Value <- all_model_results$Value * -1
}
# run best model as ensemble and save results
cat("Run the best performing model as ensemble:\n")
. = NULL
top_n_model_results <- all_model_results %>%
dplyr::top_n(n = 1, wt = "Value") %>%
dplyr::mutate("dropout" = round(., 2)) %>%
dplyr::select(-"Value")
if(nrow(top_n_model_results) != 1) top_n_model_results <- top_n_model_results[1, ]
set.seed(seed)
wt_nn(
layers = top_n_model_results$layers,
units = top_n_model_results$units,
dropout = top_n_model_results$dropout,
batch_size = top_n_model_results$batch_size,
timesteps = top_n_model_results$timesteps,
x_train = x_train,
y_train = y_train,
x_val = x_val,
y_val = y_val,
x_test = x_test,
y_test = y_test,
catchment = catchment,
epochs = epochs,
early_stopping_patience = early_stopping_patience,
ensemble_runs = ensemble_runs,
model_short = model_short,
model_name = model_name,
save_model_and_prediction = TRUE,
na_test = na_test,
na_train = na_train,
x_full_train = x_full_train,
y_full_train = y_full_train,
start_time = start_time,
seed = seed,
nn_type = "RNN",
test = test,
train = train,
type = type,
n_features = (ncol(train_data)-2),
full_train_split = full_train_split,
test_split = test_split)
}
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