# ---- Auto ADAM ----
context("TEST adam_reg: Auto ADAM")


# PARSNIP ----

# * NO XREGS ----

# TESTS
test_that("adam_reg: Auto ADAM, (No xregs), Test Model Fit Object", {

    skip_on_cran()

    # SETUP

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

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

    # Model Spec
    model_spec <- adam_reg(
        seasonal_period          = 12
    ) %>%
        set_engine("auto_adam")

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

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

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

    # $fit

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

    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))

    # $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)$value - predictions_tbl$.value

    # - Max Error less than 1500
    testthat::expect_lte(max(abs(resid)), 3000)

    # - MAE less than 700
    testthat::expect_lte(mean(abs(resid)), 1000)

})

# * XREGS ----


# TESTS
test_that("adam_reg: Auto ADAM, (XREGS)", {

    testthat::skip_on_cran()


    # Data
    m750 <- m4_monthly %>% filter(id == "M750") %>% mutate(month = month(date, label = TRUE))

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

    # Model Spec
    model_spec <- adam_reg(
        seasonal_period          = 12
    ) %>%
        set_engine("auto_adam")

    # Fit Spec
    model_fit <- model_spec %>%
        fit(value ~ date + month, data = training(splits))

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

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

    # $fit

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

    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))

    # $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)$value - predictions_tbl$.value

    # - Max Error less than 1500
    testthat::expect_lte(max(abs(resid)), 3000)

    # - MAE less than 700
    testthat::expect_lte(mean(abs(resid)), 1000)

})


# ---- WORKFLOWS ----

# TESTS
test_that("adam_reg: Auto ADAM (workflow), Test Model Fit Object", {

    skip_on_cran()

    # Data
    m750 <- m4_monthly %>% filter(id == "M750") %>% mutate(month = month(date, label = TRUE))

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

    # Model Spec
    model_spec <- adam_reg(
        seasonal_period          = 12,
        non_seasonal_ar          = 3,
        non_seasonal_differences = 1,
        non_seasonal_ma          = 3,
        seasonal_ar              = 1,
        seasonal_differences     = 0,
        seasonal_ma              = 1
    ) %>%
        set_engine("auto_adam")

    # Recipe spec
    recipe_spec <- recipe(value ~ date, data = training(splits))

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

    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))


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

    # $fit

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

    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))

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


    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 1500
    testthat::expect_lte(max(abs(resid)), 3000)

    # - MAE less than 700
    testthat::expect_lte(mean(abs(resid)), 1000)

})