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, training data, grouped with dates is correct", {
#------------------------------------------------------------------------------
# Create a simple data.frame with 1 feature.
dates <- seq(as.Date("2015-01-01"), as.Date("2020-12-01"), by = "1 month")
data <- data.frame(
"outcome" = 1:length(dates),
"group" = c(rep("A", 5), rep("B", 60), rep("C", 5), rep("B", 2)),
"feature" = 1:length(dates) * 2
)
# create_lagged_df(data_test) should equal the constructed data.
data_test <- data
# We will pass an unsorted data_test into create_lagged_df() and if we sort data now,
# the results should match the output of create_lagged_df().
data$dates <- dates
data <- dplyr::arrange(data, group, !!dates)
data$dates <- NULL
#------------------------------------------------------------------------------
# data is the ground truth dataset.
data <- data %>%
dplyr::group_by(group) %>%
dplyr::mutate("outcome_lag_1" = dplyr::lag(outcome, 1),
"outcome_lag_2" = dplyr::lag(outcome, 2),
"outcome_lag_3" = dplyr::lag(outcome, 3),
"feature_lag_1" = dplyr::lag(feature, 1),
"feature_lag_2" = dplyr::lag(feature, 2),
"feature_lag_3" = dplyr::lag(feature, 3))
data$feature <- NULL
data <- as.data.frame(data)
#------------------------------------------------------------------------------
data_out <- forecastML::create_lagged_df(data = data_test, type = "train",
outcome_col = 1, horizons = 1,
lookback = 1:3, dates = dates,
frequency = "1 month", groups = "group")
data_out <- data.frame(data_out$horizon_1)
identical(data, data_out)
})
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
test_that("lagged_df, forecasting data, grouped with dates is correct", {
lookback <- 1:3
#------------------------------------------------------------------------------
# Create a simple data.frame with 1 feature.
dates <- seq(as.Date("2015-01-01"), as.Date("2020-12-01"), by = "1 month")
data <- data.frame(
"outcome" = 1:length(dates),
"group" = c(rep("A", 5), rep("B", 60), rep("C", 5), rep("B", 2)),
"feature" = 1:length(dates) * 2
)
data$date <- dates
# create_lagged_df(data_test) should equal the constructed data.
data_test <- data
#------------------------------------------------------------------------------
# data is the ground truth dataset.
data <- data.frame(
"group" = c("A", "B", "C"),
"outcome_lag_1" = c(NA, 72, NA),
"outcome_lag_2" = c(NA, 71, NA),
"outcome_lag_3" = c(NA, NA, 70),
"feature_lag_1" = c(NA, 144, NA),
"feature_lag_2" = c(NA, 142, NA),
"feature_lag_3" = c(NA, NA, 140)
)
#------------------------------------------------------------------------------
data_test <- forecastML::fill_gaps(data_test, date_col = 4, frequency = '1 month',
groups = "group")
dates <- data_test$date
data_test$date <- NULL
data_out <- forecastML::create_lagged_df(data = data_test, type = "forecast",
outcome_col = 1, horizons = 1,
lookback = lookback, dates = dates,
frequency = "1 month", groups = "group")
data_out <- data.frame(data_out$horizon_1)
data_out[, -1] <- lapply(data_out[, -1], as.numeric)
data_out[, c("index", "horizon")] <- NULL
identical(data, data_out)
})
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
test_that("lagged_df, forecasting data, grouped with predict_future", {
lookback <- 1:3
#------------------------------------------------------------------------------
# Create a simple data.frame with 1 feature.
dates <- seq(as.Date("2015-01-01"), as.Date("2020-12-01"), by = "1 month")
data <- data.frame(
"outcome" = 1:length(dates),
"group" = c(rep("A", 5), rep("B", 60), rep("C", 5), rep("B", 2)),
"feature" = 1:length(dates) * 2,
"dynamic_feature_1" = base::months(dates)
)
data$date <- dates
# create_lagged_df(data_test) should equal the constructed data.
data_test <- data
#------------------------------------------------------------------------------
# data is the ground truth dataset.
data <- data.frame(
"group" = c("A", "B", "C"),
"outcome_lag_1" = c(NA, 72, NA),
"outcome_lag_2" = c(NA, 71, NA),
"outcome_lag_3" = c(NA, NA, 70),
"feature_lag_1" = c(NA, 144, NA),
"feature_lag_2" = c(NA, 142, NA),
"feature_lag_3" = c(NA, NA, 140),
"dynamic_feature_1" = c("January", "January", "January") # Predicted with predict_future.
)
#------------------------------------------------------------------------------
data_test <- forecastML::fill_gaps(data_test, date_col = 5, frequency = '1 month',
groups = "group")
dates <- data_test$date
data_test$date <- NULL
predict_future <- function(data, index) {
data <- data.frame("index" = as.Date("2021-01-01"),
# "group" = c("A", "B", "C"), # Optional
"dynamic_feature_1" = "January")
}
data_out <- forecastML::create_lagged_df(data_test, type = "forecast",
outcome_col = 1, horizons = 1,
lookback = lookback, dates = dates,
frequency = "1 month", groups = "group",
dynamic_features = "dynamic_feature_1",
predict_future = predict_future
)
data_out <- data.frame(data_out$horizon_1)
data_out[, -c(1, ncol(data_out))] <- lapply(data_out[, -c(1, ncol(data_out))], as.numeric)
data_out[, c("index", "horizon")] <- NULL
identical(data, data_out)
})
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