Nothing
#------------------------------------------------------------------------------
# Test that create_lagged_df() produces correct lags for training and forecasting
# datasets that have dates and are ungrouped.
library(forecastML)
library(dplyr)
test_that("lagged_df, forecasting data, non-grouped with daily date horizons are correct", {
#------------------------------------------------------------------------------
# Create a simple data.frame with 1 feature.
dates <- seq(as.Date("2019-01-01"), as.Date("2020-12-01"), by = "1 day")
# create_lagged_df(data_test) should equal the constructed data.
data_test <- data.frame(
"outcome" = 1:length(dates),
"feature" = 1:length(dates) * 2
)
#------------------------------------------------------------------------------
# data is the ground truth dataset.
data <- data.frame(
"index" = c("2020-12-02", "2020-12-03", "2020-12-04"),
"horizon" = 1:3
)
data$index <- as.Date(data$index)
#------------------------------------------------------------------------------
data_out <- forecastML::create_lagged_df(data = data_test, type = "forecast",
outcome_col = 1, horizons = 3,
lookback = 3:4, dates = dates,
frequency = "1 day")
data_out <- data.frame(data_out$horizon_3[, c("index", "horizon")])
data[, 2] <- as.numeric(data[, 2])
identical(data, data_out)
})
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
test_that("lagged_df, forecasting data, non-grouped with monthly date horizons are correct", {
#------------------------------------------------------------------------------
# Create a simple data.frame with 1 feature.
dates <- seq(as.Date("2019-01-01"), as.Date("2020-12-01"), by = "1 month")
# create_lagged_df(data_test) should equal the constructed data.
data_test <- data.frame(
"outcome" = 1:length(dates),
"feature" = 1:length(dates) * 2
)
#------------------------------------------------------------------------------
# data is the ground truth dataset.
data <- data.frame(
"index" = c("2021-01-01", "2021-02-01", "2021-03-01"),
"horizon" = 1:3
)
data$index <- as.Date(data$index)
#------------------------------------------------------------------------------
data_out <- forecastML::create_lagged_df(data = data_test, type = "forecast",
outcome_col = 1, horizons = 3,
lookback = 3:4, dates = dates,
frequency = "1 month")
data_out <- data.frame(data_out$horizon_3[, c("index", "horizon")])
data[, 2] <- as.numeric(data[, 2])
identical(data, data_out)
})
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
test_that("lagged_df, forecasting data, non-grouped with incorrect/missing args throws errors", {
#------------------------------------------------------------------------------
# Create a simple data.frame with 1 feature.
dates <- seq(as.Date("2019-01-01"), as.Date("2020-12-01"), by = "1 month")
# create_lagged_df(data_test) should equal the constructed data.
data_test <- data.frame(
"outcome" = 1:length(dates),
"feature" = 1:length(dates) * 2
)
#------------------------------------------------------------------------------
horizons <- 7
lookback <- 3
# Expect that feature lags support direct forecasting to the given horizon
expect_error(forecastML::create_lagged_df(data = data_test, type = "forecast",
outcome_col = 1, horizons = horizons,
lookback = lookback, dates = dates,
frequency = "1 month"))
horizons <- 7
lookback <- 10
dates_test <- as.character(dates)
# Expect that a non-date vector of throws an error.
expect_error(forecastML::create_lagged_df(data = data_test, type = "forecast",
outcome_col = 1, horizons = horizons,
lookback = lookback, dates = dates_test,
frequency = "1 month"))
# Expect that specifying both lookback and lookback control throws an error.
expect_error(forecastML::create_lagged_df(data = data_test, type = "forecast",
outcome_col = 1, horizons = horizons,
lookback = lookback, lookback_control = lookback,
dates = dates,
frequency = "1 month"))
})
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