tests/testthat/test-algo-nnetar_reg.R

# ---- STANDARD ARIMA ----
context("TEST nnetar_reg")


# SETUP ----

# Data
m750 <- m4_monthly %>% filter(id == "M750")

# Split Data 80/20
splits <- initial_time_split(m750, prop = 0.8)

# Model Spec
model_spec <- nnetar_reg(
    seasonal_period          = 12,
    non_seasonal_ar          = 3,
    seasonal_ar              = 1,
    hidden_units             = 6,
    num_networks             = 15,
    penalty                  = 0.1,
    epochs                   = 50
) %>%
    set_engine("nnetar")


# PARSNIP ----

# * NO XREGS ----

# Fit Spec
set.seed(123)
model_fit <- model_spec %>%
    fit(log(value) ~ date, data = training(splits))

# Predictions
predictions_tbl <- model_fit %>%
    modeltime_calibrate(testing(splits)) %>%
    modeltime_forecast(new_data = testing(splits))


# TESTS
test_that("nnetar_reg: (No xregs), Test Model Fit Object", {

    testthat::expect_s3_class(model_fit$fit, "nnetar_fit_impl")

    # $fit

    testthat::expect_s3_class(model_fit$fit$models$model_1, "nnetar")

    testthat::expect_s3_class(model_fit$fit$data, "tbl_df")

    testthat::expect_equal(names(model_fit$fit$data)[1], "date")

    testthat::expect_true(is.null(model_fit$fit$extras$xreg_recipe))


    testthat::expect_identical(model_fit$fit$models$model_1$p, 3)
    testthat::expect_identical(model_fit$fit$models$model_1$P, 1)
    testthat::expect_identical(model_fit$fit$models$model_1$size, 6)

    # nnets
    testthat::expect_identical(length(model_fit$fit$models$model_1$model), 15L)
    testthat::expect_identical(model_fit$fit$models$model_1$model[[1]]$decay, 0.1)


    # $preproc

    testthat::expect_equal(model_fit$preproc$y_var, "value")


    # Structure
    testthat::expect_identical(nrow(testing(splits)), nrow(predictions_tbl))
    testthat::expect_identical(testing(splits)$date, predictions_tbl$.index)

    # Out-of-Sample Accuracy Tests

    resid <- testing(splits)$value - exp(predictions_tbl$.value)

    # - Max Error less than 1500
    testthat::expect_lte(max(abs(resid)), 1600)

    # - MAE less than 700
    testthat::expect_lte(mean(abs(resid)), 700)

})

# * XREGS ----

# Fit
set.seed(123)
model_fit <- model_spec %>%
    fit(log(value) ~ date + month(date, label = TRUE), data = training(splits))

# Predictions
predictions_tbl <- model_fit %>%
    modeltime_calibrate(testing(splits)) %>%
    modeltime_forecast(new_data = testing(splits))


# TESTS
test_that("nnetar_reg (XREGS)", {

    testthat::expect_s3_class(model_fit$fit, "nnetar_fit_impl")

    # $fit

    testthat::expect_s3_class(model_fit$fit$models$model_1, "nnetar")

    testthat::expect_s3_class(model_fit$fit$data, "tbl_df")

    testthat::expect_equal(names(model_fit$fit$data)[1], "date")

    testthat::expect_true(!is.null(model_fit$fit$extras$xreg_recipe))

    testthat::expect_identical(model_fit$fit$models$model_1$p, 3)
    testthat::expect_identical(model_fit$fit$models$model_1$P, 1)
    testthat::expect_identical(model_fit$fit$models$model_1$size, 6)

    # nnets
    testthat::expect_identical(length(model_fit$fit$models$model_1$model), 15L)
    testthat::expect_identical(model_fit$fit$models$model_1$model[[1]]$decay, 0.1)

    # $preproc

    testthat::expect_equal(model_fit$preproc$y_var, "value")

    # Structure
    testthat::expect_identical(nrow(testing(splits)), nrow(predictions_tbl))
    testthat::expect_identical(testing(splits)$date, predictions_tbl$.index)

    # Out-of-Sample Accuracy Tests

    resid <- testing(splits)$value - exp(predictions_tbl$.value)

    # - Max Error 967.2171
    testthat::expect_lte(max(abs(resid)), 1250)

    # - MAE 407.0114
    testthat::expect_lte(mean(abs(resid)), 500)

})


# ---- WORKFLOWS ----

# Model Spec
model_spec <- nnetar_reg(
    seasonal_period          = 12,
    non_seasonal_ar          = 3,
    seasonal_ar              = 1,
    hidden_units             = 6,
    num_networks             = 15,
    penalty                  = 0.1,
    epochs                   = 50
) %>%
    set_engine("nnetar")

# Recipe spec
recipe_spec <- recipe(value ~ date, data = training(splits)) %>%
    step_log(value, skip = FALSE)

# Workflow
wflw <- workflow() %>%
    add_recipe(recipe_spec) %>%
    add_model(model_spec)

set.seed(123)
wflw_fit <- wflw %>%
    fit(training(splits))

# Forecast
predictions_tbl <- wflw_fit %>%
    modeltime_calibrate(testing(splits)) %>%
    modeltime_forecast(new_data = testing(splits), actual_data = training(splits)) %>%
    mutate_at(vars(.value), exp)



# TESTS
test_that("nnetar_reg: (workflow)", {

    testthat::expect_s3_class(wflw_fit$fit$fit$fit, "nnetar_fit_impl")

    # $fit

    testthat::expect_s3_class(wflw_fit$fit$fit$fit$models$model_1, "nnetar")

    testthat::expect_s3_class(wflw_fit$fit$fit$fit$data, "tbl_df")

    testthat::expect_equal(names(wflw_fit$fit$fit$fit$data)[1], "date")

    testthat::expect_true(is.null(wflw_fit$fit$fit$fit$extras$xreg_recipe))

    testthat::expect_identical(wflw_fit$fit$fit$fit$models$model_1$p, 3)
    testthat::expect_identical(wflw_fit$fit$fit$fit$models$model_1$P, 1)
    testthat::expect_identical(wflw_fit$fit$fit$fit$models$model_1$size, 6)

    # nnets
    testthat::expect_identical(length(wflw_fit$fit$fit$fit$models$model_1$model), 15L)
    testthat::expect_identical(wflw_fit$fit$fit$fit$models$model_1$model[[1]]$decay, 0.1)

    # $preproc
    mld <- wflw_fit %>% workflows::pull_workflow_mold()
    testthat::expect_equal(names(mld$outcomes), "value")

    # Predictions

    full_data <- bind_rows(training(splits), testing(splits))

    # Structure
    testthat::expect_identical(nrow(full_data), nrow(predictions_tbl))
    testthat::expect_identical(full_data$date, predictions_tbl$.index)

    # Out-of-Sample Accuracy Tests
    predictions_tbl <- predictions_tbl %>% filter(.key == "prediction")
    resid <- testing(splits)$value - predictions_tbl$.value

    # - Max Error less than 1501.464
    testthat::expect_lte(max(abs(resid)), 1600)

    # - MAE less than 700
    testthat::expect_lte(mean(abs(resid)), 700)

})

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modeltime documentation built on Sept. 15, 2021, 1:06 a.m.