Nothing
#------------------------------------------------------------------------------
# Test that return_hyper() works correctly.
library(forecastML)
library(dplyr)
test_that("return_hyper returns a data.frame of parameters with 1 row for each model.", {
set.seed(224)
data_seatbelts <- data.frame("y" = rnorm(1:200))
#data_seatbelts <- data_seatbelts[, 1:2, drop = FALSE]
# Example - Training data for 2 horizon-specific models w/ common lags per predictor.
horizons <- c(1, 12)
lookback <- c(1, 3, 6, 9, 12, 15)
data_train <- create_lagged_df(data_seatbelts, type = "train", outcome_col = 1,
lookback = lookback, horizon = horizons)
windows <- create_windows(data_train, window_length = 0)
model_function <- function(data) {
model <- lm(y ~ ., data = data)
return(model)
}
set.seed(224)
model_results <- train_model(data_train, windows, model_name = "LM", model_function)
hyper_function <- function(model) {
# Hardcoded because we are interested in the function return, no the model parameters.
data_hyper <- data.frame("test" = 1)
return(data_hyper)
}
data_hyper <- return_hyper(model_results, hyper_function)
expect_true(is.data.frame(data_hyper))
expect_true(all(c("test") %in% names(data_hyper)))
expect_true(nrow(data_hyper) == nrow(windows) * length(horizons))
})
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